{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def get_site_attr(site_name, attr):\n",
    "    basic_info_df = pd.read_csv('generated\\\\data\\\\parkings_info.csv')\n",
    "    return basic_info_df.loc[basic_info_df.parking_name == site_name].iloc[0][attr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "from shutil import copyfile\n",
    "\n",
    "folders = glob.glob('generated\\\\week\\\\2000*')\n",
    "lines = []\n",
    "for folder in folders:\n",
    "    full = glob.glob(folder+'\\\\raw_*')[0].split('_')\n",
    "    target = full[0][19:]\n",
    "    area = full[5:-1]\n",
    "    path = 'D:/data/generated/'+target+'_compare_all_2000.png'\n",
    "    copyfile(folder+'\\\\compare_all_2000.png', path)\n",
    "    for sub in area:\n",
    "        lines.append([target, sub, get_site_attr(sub,'total_space'),\n",
    "                      get_site_attr(sub,'monthly_fee'), get_site_attr(sub,'building_type'),\n",
    "                     0,0,path])\n",
    "\n",
    "df = pd.DataFrame(lines)\n",
    "df.columns = ['target', 'area', 'total_space', 'monthly_fee', 'building_type','corre','icorre','result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('./correlation_analysis.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['generated/week\\\\2000万山珠宝工业园_True_3_5376_1609284399.7520025',\n",
       " 'generated/week\\\\2000万达丰大厦_True_14_5376_1609200082.5138874',\n",
       " 'generated/week\\\\2000东翠花园_True_2_5376_1609156489.6720402',\n",
       " 'generated/week\\\\2000中信星光明庭管理处_True_9_5376_1609235944.7706044',\n",
       " 'generated/week\\\\2000中深石化大厦_True_2_5376_1609228667.1645172',\n",
       " 'generated/week\\\\2000丰园酒店_True_10_5376_1609168857.3625247',\n",
       " 'generated/week\\\\2000化工大厦_True_3_5376_1609187166.1566265',\n",
       " 'generated/week\\\\2000华瑞大厦_True_4_5376_1609256379.276467',\n",
       " 'generated/week\\\\2000同乐大厦_True_9_5376_1609263244.0220852',\n",
       " 'generated/week\\\\2000天元大厦_True_9_5376_1609193393.997863',\n",
       " 'generated/week\\\\2000文锦广场_True_9_5376_1609207148.6420422',\n",
       " 'generated/week\\\\2000新白马_True_11_5376_1609270182.9053128',\n",
       " 'generated/week\\\\2000桂龙家园_True_4_5376_1609291617.3268278',\n",
       " 'generated/week\\\\2000武警生活区银龙花园_True_2_5376_1609221575.952158',\n",
       " 'generated/week\\\\2000永新商业城_True_9_5376_1609214328.745912',\n",
       " 'generated/week\\\\2000洪涛大厦_True_8_5376_1609181199.2818165',\n",
       " 'generated/week\\\\2000红围坊停车场_True_10_5376_1609174927.301197',\n",
       " 'generated/week\\\\2000翠景山庄_True_2_5376_1609249504.6663668',\n",
       " 'generated/week\\\\2000都市名园_True_8_5376_1609242723.467352',\n",
       " 'generated/week\\\\2000都心名苑_True_17_5376_1609162405.4070945',\n",
       " 'generated/week\\\\2000银都大厦_True_9_5376_1609277339.484834']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jpgFilenamesList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-07-01 08:45:00 2016-12-31 23:45:00\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABYUAAAQNCAYAAAACISXYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAN1wAADdcBQiibeAAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde3yP9f/H8ednZ4wwhjk3JUwo5GzMYTPMIbJok5z6SXQgHSip5JAQKoUmtTnExjCbLeQQIYdQOR93YJrYeft8fn/Ivmkz28zxetxvt91uXNf7el+vzwd9up6f63q9TRaLRQAAAAAAAAAAY7C62wUAAAAAAAAAAO4cQmEAAAAAAAAAMBBCYQAAAAAAAAAwEEJhAAAAAAAAADAQQmEAAAAAAAAAMBBCYQAAAAAAAAAwEEJhAAAAAAAAADAQQmEAAAAAAAAAMBCbvAwymUyW210IAAAAAAAAAODWWSwWU2778xQK/zPRrVcDAAAAAAAAALhtTKZc82BJtI8AAAAAAAAAAEMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADIRQGAAAAAAAAAAMhFAYAAAAAAAAAAyEUBgAAAAAAAAADsbnbBQAAAAAA7m/7ziQodF+0dp68qCNxV5SWaZadtZVqODuqYdXS6lLPRXUrPnS3ywQAAP8wWSyWmw8ymSx5GQcAAAAAMI5fTlzUuJW/6VD0Zdlam5Semf268dr22hVK6P2uddSwWum7UCkAAMZhMplksVhMuY4hFAYAAAAA5EdGplkTw37XvM3HZWWSzHm4XLw27oUW1fWm52OysaabIQAAt0NeQmE+hQEAAADgAZKenn5Lx587dy7X/RmZZv3f97sVsPWEpLwFwv8eF7D1hP7v+93KyDRLki5evKh9+/YVtNxsEhISlJmZefWcZnOhzQsAwIOEUBgAgNtg35kEfbTmkHp8vkWPj1+nx8at1ePj16nH51v00ZpD2n/20t0uEQDwALp8+bJcXV21ePFiSVJKSopMJpM2bdqUNSY5OTnr13/88YfeeecdZWRkSJIiIiLUoEEDXbx4Uenp6YqOjtZff/2lhISErJ8P1xxU1O9xSk9LlcWcmae6LJkZMqelSJIyzBZF/R6nj8N+l8Vi0dChQ9W7d2/Fxsbm+XXWqlVLa9euzXHf888/r48//ljTp09X+/btr3u9AADgKhaaAwCgEOXWWzEl3azdpxK0/+wlzf3pGL0VAQCFbvTo0SpWrJg6d+4sSbK3t5cklSxZUpL0zTffaPTo0QoNDVXjxo1Vrlw57d69Wz4+Plq+fLnat2+vli1batq0afL19VWDBg1UpEgRmUxXn0C9dOmSKg7+UjalK+r8iolKPvqLZPrnXqNrLQdNOTytajHLqkgJVR7xvaSrwfDXPx3V3qBPtD4iQuXLl5eHh4ckKTU1VadPn9bbb7+tsWPH5vg6ixYtqiJFiuS4z9HRUY6Ojho2bJj279+v33//XQ0aNMj/mwkAwAOMnsIAABQCeisCAO62r7/+WoMGDdKECRP0zjvvZG03mUyaOnWqli1bJmtra7311lvy8vLKCnrNZrMOHjyoEiVK6NVXX9XXX3+dFSL/l8lkUuVhC2RVvKwsmRkyWf/vPqO4Ze/LtkwVlXLvn+04i8UsZWbIZGMnScpMuqT40Gmyy0jUoZ+jVKFChayx/fv3V2xsrNasWZNV4381bNhQU6dOlbu7uzw9PbVu3bobvi916tTRb7/9duM3DgCAB0xeegpzpzAAALfoWm/FqN/jJBWst+Lpi0ma8+wTBMNAAWRmZspiscjG5ub/a5uQkKDExEQVL15cJUqUuAPVAXfG5MmT9fHHH8vNzS1r26lTpxQYGChJWr16tSZOnCh3d/frjktLS5OdnZ3c3Nx09OhR/fDDD/r++++VmJgoBwcHWVtby2w26+LFizqXcvXfmNlikpV0XSAsSZaMNFnZF82xPpPJSroWCCf/rZiAV2VOuaJUK2u1bOMhB5urn3/Hjx+Xk5OTHB0dVbduXV28eFGenp6aP3++JGn37t1asGCBzpw5o1mzZun333+Xg4ODFixYoP79+0uSxowZI0maOHGi0tPT6SsMAEAOuPIEAOAfmZmZio+Pz/dxE8N+V9TvccrIaxr8L5bMDKWnp2X1ViwMFouF/okwBLPZrK+//loVKlRQw4YNtWXLllzHHz9+XJ6enmrWrJlGjBihtLS0O1QpcHvFxcVp6dKlWrFihVxdXSVJu3btUuPGjbM+12bOnHldILxjxw7t2rVLjz76qDZv3izpf60m0tLSVLp0aZUsWVImk0klS5ZUnTp1FLovOtc6zClXZLK2kzktOdvPv1kXKaHyflNV7PF2KvZII73w6Q/67bffNG/ePJUrV07Hjh3TwYMH9dtvv2nw4MHX3S1cqlQpPfnkkypatKhq1qwpV1dXWVldf1nr5OSkixcvymQy6a233tLhw4cL9sYCAPAAIxQGAOAfs2fPlqurqy5cuJDnY345cVFzw3bpxGd+Sk+Iyfc5086f0KkpPXTlxH7N23xcO09czHFcdHS0TCaTqlWrdtOfqlWrqm7duvmuBbjfHDhwQK+99pp27typlStXavjw4Tccm5qaqtdee03NmzfXyZMnVbt2bX3yySd3sFrg9nF2dtYvv/yi1q1bZ2178sknde7cOU2ePFmSdOTIkax9J0+eVLNmzfT777/r2WefVZs2bTRr1qys/Y6OjkpNTVV09NUQ+O+//1ZsbKx2nsz5M+qajEtx+ivqa52e1us/P72zLUhnXayULJkZSvzjZ338grfc3Nzk7e2thIQE1a9fX25ubnJzc9OcOXOu+wKnevXq6t+/v0qXLq327durffv2MplMeuGFF2RjY6OHHnpIFSpUUHx8vDIyMjRz5swCfeELAMCDjvYRAABI2r9/v8aOHavGjRtr6NChCgoKytOj6ONW/iYrKytlXo6/+mhsPlnZF5NkkU3J8jKZpHErD2jNyy2zjbO3t5e1tbW2bt2q0NDQrN/nJC0tTTVq1Mh3LcD9Zt26derWrZuqVKmilJQUtWnTRhs2bMj2eLx0NRTesGGDvvrqK0nSgAED1LlzZ7355pt3uGrg9kpLS1N6erqkq/0EMzMz5enpqR49eujf68Q88cQTat26tTp06CAXFxdVrVo1KwQ+ceKEkpKSlJqaKknat2+f0tPT9Uf0pRueN/PKXzIn/y2XwV/KtnTFrO3JJ/bo/IqPZLLK/pnl1OFFlW47UOYjPyn+5+9lsVh07NixrH7G+/fvV3h4uF577bWbvuZFixapQ4cOevjhh1W1alUdOnRIx48fl4ODg5o3b57Hdw8AAOMgFAYAGN7JkyfVpUsXjR07ViNHjlTv3r3VrVs3BQYGqnjx4jc8bt+ZBB2KviyL1T8fpwUIha+xsi8qs0U6GP239p+9pLoVH7p+/z+PxprN5qyLfSsrK61cuVJxcXEaOHBg1tjU1NSsMcCDLDo6WvXr15ck2djYqGLFijp8+HCOoXBKSooyMjLk5OQkSXJwcFBCQsKdLPe+ZLFYZLFYlJmZKbPZLLPZfN2v//v7+3HfvVpXQfelpaUpLCxMEyZMUG6Lhe/evVuVK1fOcV/16tWv+329evUkSZVfW3bD+VJO7pVVsZLXBcKSZElNkpW9Y7bxSUd26MrecKWe/k1FKj2m5QEBeuWVV9S1a1dFRERo5cqVGjp0qPr166fU1NSs1hY5uXTpkhwdHZWeni47OzvVqVNHf/75pyIiItS2bVvZ2tre8FgAAIyKUBgAYGj79+9X586dNXDgQL3++uuSpMWLF2vo0KFq0KCBPvvsM3l5eWWNnzRpkpYsWaJdu3YpdF+0bK1NSr3B3NEBr8ixvpeK1+sgSTKnJEpWVjLZOvyvP6I5Q5Jksr16sWtrbVLIr6dUtXhVpaamqmzZstfNWalSJVWtWjVrEZ1z587J2tpa06dPzxrj7u6uYcOG3fJ7AxSGa6HirYZf5cuX10MPPZRt7mtf3FhZWalYsWI3DHqtrKxUtOj/FsCytra+4ZcnV65c0bfffntPBX13KyjNLVS8XUwmk6ysrGRtbS0rK6tsv77T+240ztbW9q7W9e/fX/t3dm1huGv79u/fr48++kjLly+/7jgrKytlZGTI3t5etra2+uWXX3Ts2DG1b99erVq10vHjx2VlZaWUlBTVrFlTMTExsra2VptZO3X6Bn9uV36LUpHqT2bbbk5NlJVD9lDYvuJjyrh4TqU8BqpI+t969dVX5e7urkOHDql69epKSkqSt7e3ZsyYccO/K6tWrdIff/yhP//8U5UqVVJ6errs7e1VunRpVa9eXRMnTtS4ceMK+lcRAIAHGqEwAMCQzGaz5syZozfeeEMjR47USy+9dF2YNHnyZE2cOFGdOnVS48aNNWTIED399NOysbHJCkl2nryo9MwbBybmtGTJ8r8Vz+PXzlDSH1tzHHtqsk/Wr8f+8+Pk5JRjf+OUlBRZWVll9Yn8t3Xr1unMmTM3e/n3lX+HivdiaGaEfbcyR2GFiosWLVLfvn2v21aqVCldvPi/HqfJyck3bPtyrUfqNVZWVtf9/t8SExM1c+bMQgsPb7TPxsZGdnZ290yweDtfa173mUym6xYVQ97s3LlTjRo1uuH+Fi1a5Lj98OHDqlGjhsaMGaOaNWtm3SFcqVIlWSyWrH8jzs7OSk9PV42yxfRbDvOkRh9WyvFfVd5/WrZ95pQrsi6SPRROO/eHzCmXdWHFRDnYSN9/OUNms1nff/+9UlNTderUKbm5uWnXrl168sn/hc0bNmzQ2LFjdejQITk7O8vR0VEpKSlydHRUcHBw1pdHrVq1UlBQkHr16iWz2SwrK5bTAQDg3wiFAQCGExcXp9atWys+Pl5jx47VuHHjNHPmTElX7xC8dkGZmpqqefPm6eDBg3r//ffVvn37rABDko7EXcn1PCaTlfSvcKN0+xdVqt1gmUz/66uYdPQXXVw7U869x8vO+WFJkr2VWS+UP6uMjAzNnj1bZrNZV65ckcVi0dSpU7Vv3z6dOnVKH3zwQVZgeu0nLi5O9vb2GjVq1H0XHt5on9lszvbe3m7X7lS8F0Ozf++zsbG5JwLCO/E+lCpVKtuf01NPPaWZM2dq9OjRMpvN2rFjh3r16pXjn6mNjY0aNmyoqKgotW3bVrt3785qJfFf5cqV06FDhwr17xRwO9WrV08xMTHZWiz8/PPPGjx4sPbt23fddrPZrNTUVDk7O2vPnj2KjIzUvHnzshZ0Cw0N1fDhw2Vtba2qVauqevXqV+8a9sn+FIolI10Xw2bJ4eEnZF/hkWz7M5MuyarIQ9m2p/8VrczEv1S2w2D1alFHCxcu1JEjR7Rs2TK1a9dOxYsX12effaauXbtq7ty58vb2lnS1tYW7u7uWLVumcuXKqUOHDurbt6+ioqL09ttva+rUqdq0aZOCgoIkXW2T8d1332nQoEFq0qRJwd5gAAAeQITCAADDcXZ21vjx49WuXTuVLl06qxXDiRMnVKNGjRwfP58yZUq2u9fSMvMXVlo7Zg+10mOPSVbWSj66U0UevnonVEZGmhYsWHBdMHbtTtmgoCAlJCTo8uXL2rNnz3U1mUwmpaenq2TJkjp48GCeAzs7O7v7LiC83fu4U/H+0Lp1a7344ouaM2eOihUrpk2bNmnu3LlKSUnJerT/GhsbGw0YMEBvvPGG3nzzTc2aNUsvvPDCXaweKDy2trYqV65ctu3XWqZcW7jtvywWi1577TX16dNHzs7OOnXqlCSpa9eu6tatmy5duqSlS5cqICBAmzdvVok9G64eaGWVdXx82CxlXIpVhafH5jC/WWkxR2TnXD3bviI1GstkY6f4LUu0dleqxr41Rr6+vgoPD5eDg4Ps7e3Vq1cvXblyRc8884zKli2rTZs2qWrVqpowYYKkq0/17NixQ998842cnJzk7++vb7/9Vp06ddKUKVPk4OCgLl26KDU1VUOGDMn3+woAwIOMUBgAYEi9e/fWlStXFBsbKzs7O5lMJv3999+SlBUKWywWpaeny8HBQSVKlMg2h521lVLSC34XqznlihIPbpCT18u6GPGFij/hLVunSnqoeDHt/fPP68ZGR0fL1dVVO3fu1JIlSzRu3LisMPvfNm3apEuXLumHH34ocF3A/cLe3l5hYWF69dVXs8IrR0dHPf300xo4cKA8PT2vG+/r66srV65o+vTpatu2rYYPH36XKgfujNTU1Bu2SZGk06dPq3jx4ho5cqSkqy1YJGnlypVavHixgoODVaxYMfn6+qply5ZaunSpKjXrIhvHUrJIil8zQ0l/bJHz0+NkU7zMdXPHLX1PKWcOSjKpVJsB2c6ddOgnpcUcVt2Oz2r77FcUFxenRx99VCdOnNAbb7yRNe7555+Xp6enfv7552wL41WtWlXffPONXFxcrs6ZlKTg4GB9+eWXWe1mMjIy9Msvv6hx48b5fv8AAHiQmfLS581kMlnuxiITAADcTl9++aVGjBghe3t7mUwmmc1mXb58Oat9hMViUVpamsaMGaN3331XkjR9+nQtWrRIO3fuVI/Pt2j3qQSZU67o9PQ+qvh/38imxP8uis99/X8q3shHxet1zPH8F8M/V1rccZXvN1l/Rc1T6tlDKtdvshpWc9IPQ5tdN3bPnj3y8vJSdHS09u/fr5CQEH333XeytbVV7969rxv7yCOP6JlnninMtwoA8IA7cOCAAgICtGjRIp0/f17e3t7y9/eXt7e37OzsssbtPHFRvb7cJoukjL/PK/PKRdm71Mw2X+rZQ8pMviyHym6ysi+abb8kmSQtHdJUDauVliSFh4erdu3aqlSp0u14iQAAGIbJZJLFYsn18UfuFAYAGNaQIUNUu3ZtnTx5Uv369cvWPuLTTz9VhQoV1KdPn6xj/v0lacOqpbX/7CXd+B6sG7uyP1JX9keqQv9PJUkPteyr6G9G6mLYHKV2eVpHjjirRo0aWeP37NmjRx99VNLVO6POnz+v33//XS4uLll9E6Wri2lxlzAAIC8uXLigwMBABQQEZC3o9uabb8rX11dlypTJ8ZgnqpRUtdRjOmZTRTYlysqmRNkcx9lXrJXruU0Wswa0eDgrEJakDh06FPzFAACAfGEJVgCAoVlZWenFF1/Ujh07rtt+4cIFjR8/XkWKFLlu+7VFeCSp8+MVlJ55kydp/vOkjcVi1qWtSxS/9jM5eb4kW6erj8Ja2TrIucfbSvz9J236/B098sgjaty4saZNm6azZ89q+fLlatWqlT777DO5uroqISFBzZo1U/v27bV161a9/vrrSktL06effqqKFSvewjsCAHiQpaWlacWKFerWrZsqVKigiRMnqm3btvrtt9+0c+dODR8+/IaBcEZGhgYMGKBd88aqoYuDbKwK1n/d2iSlHt+l/d9OyLW9BQAAuH1oHwEAMKTk5GRdvnxZ9vb2+uCDD3TlyhUNHz5cjz/+uBISEjRixAilp6drxowZysjIkMlkUpkyZTRhwgSNGzcuz+cp3X6oij/ZWRZzppKP/KKEzd8p469oOXUaoWK1Wl431soklU85o0MLxqhUqVJq1KiRfv75Z506dUomk0nvvfde1l1UJUqU0N69ezVq1CjFxcXJ1tZW48ePV8uWLeXk5CSTyaSyZcvm2AsZAGAsFotFu3btUkBAgAIDA5WYmKju3bvL399fHh4esrG5+QOkaWlp6tevnyIjI7Vu3TrVb/CEnv7wO/2aVErWViaZ83C5aGW6+l3pCy2qy7tiurw7ealWrVpasWIFn1cAABSivLSPIBQGABjS4sWL1a9fP9nb2+d6MXxtsbm6detq+/bt2fb/u7fif537+v/k+HgHlWjcTel/RSsm4FXZlq0qp04vy7aUS7bx13orlsr8S88++6wcHR21du1aRUVF6eOPP9ahQ4d0/vx5WSwWOTs7q0WLFvL29lazZs20ZcsWrV69WgcOHNDZs2dlb2+vI0eOqFSpUrfwLgEA7mdnz57VokWLtHDhQh08eFAtWrSQv7+/evXqldU/Py9SUlLUq1cv7dixQ+vXr1fdunV17tw51axZUy+/P0O7rWvoUPRlWckscw4Po5osZllMVqpdoYTe71onq2XEiRMn1LFjRzk6OmrNmjUqV65cob12AACMjFAYAIA74J0fdmvRjjOSlXWu4zIuX8i2Ovs1FnOGutcupen+rSQp26J30tXHdtevX6/AwECFhIQoLS1NnTt3lq+vr7y8vOTg4JA1NjMzU9bWudcDAHjwJCUlKTg4WAEBAVq/fr2qVq0qPz8/Pffcc3J1dc33fImJifLx8dEff/yhyMjIrP72vr6++vPPP7Vjxw4dPnxY9dw76+VPFurYZelI3BWlZmTK3sZaxTIv68yvmxQ6Z7wer1Qy2/znz59Xp06d9Ndffyk8PFwPP/zwLb8HAAAYHaEwAAC3WXx8vNp16Ki/6/aSKrop05z/OWysTHK8dFynAt/VmtWhatKkyU2PSUlJUVhYmAIDA7Vq1SrZ2tqqR48e8vX1Vdu2bfP0KDAA4MFgsVj0008/KSAgQEuXLpUk9erVS/7+/mrRooWsrAq2lMylS5fk7e2t6OhoRUZGqlq1apKkqKgotWvXTtu2bdNTTz2lqVOnau7cufrzzz+zzXH69GlVqVJFO3fu1JNPPpnjea5cuaKePXtq7969CgsLU/369QtULwAAuCovoTALzQEAUEBxcXFq06aNLJkZ2jzpefVvVl3S1Z6JeXG1t6JZdWzPa/u0werRvZvatWun9evX3/RYBwcHdevWTYsXL1ZsbKzmzJmj8+fPy9vbWy4uLho2bJg2b94ss7kAKTUA4L5w7Ngxvffee3J1dZW7u7tOnz6tzz//XDExMZo3b55atWpV4EA4Pj5eHh4eio+P16ZNm7IC4bS0NL300kt64YUX9NRTT0mSQkND5e3tneM8lStX1hNPPKGQkJAbnsvR0VGrVq1S27Zt1apVK/34448FqhkAAOQdoTAAAAVw7tw5tW7dWnZ2doqKilKFcs4a611bS4c01WPlry6WY8lIz/FYS+bV7Y+VL6GBVf9W2MTB+vOP3/XVV1/p//7v/+Tt7a0ffvghz7UUL15cffv2VWhoqGJiYvTBBx/o4MGDatWqlapXr67Ro0fr119/FU/9AMD979KlS1mBr6urq5YsWaIhQ4bo9OnTCg8PV9++fVW0aNFbOkdMTIzc3d2VkZGhjRs3qmLFiln7ZsyYodjYWE2cOJE2l4IAACAASURBVFGSlJCQoM2bN6tz5843nM/HxyfXUFiS7OzstGjRIg0YMECenp5atmzZLb0GAACQO9pHAACQT6dPn1bbtm1VpkwZhYWF5bhYz4eff6s5oT+rSedndeR8YlZvxRrOjjrz60ZVSDun4AWfyWKxqHv37jpx4oR27NghOzs7TZo0SW+99Zbmzp2rF154ocB1nj17VkuWLFFQUJB27NihmjVrytfXV76+vlk9IQEA977MzEytX79eAQEBWrFihYoWLSpfX1/5+/urYcOGMpny+IhKHpw+fVrt2rVTyZIlFRYWdt2CpWfOnNFjjz2madOmafDgwZKuLtw6aNAgXbhwQXZ2djnOuXfvXtWvX1/Hjh1T9erVcz2/xWLRpEmT9Pbbb2v27NkaOnRoob02AACMgp7CAAAUsuPHj6tt27aqXLmyVq9ereLFi+c4rnfv3rK3t9e3336bbd+iRYv00ksvKS4uTnZ2doqLi1OdOnU0ZMgQffDBB5KkuXPn6sUXX9SkSZP0+uuv33LdR48eVVBQkAIDA3XgwAE98cQT8vX11TPPPKPKlSvf8vwAgMJ38OBBBQQEaNGiRYqLi5O3t7f8/f3VqVMn2dvbF/r5jh49Kg8PD1WtWlWrVq1SiRIlrtvfu3dvnThxQtu2bctazPS5555TcnJyrnf2WiwWVa9eXa+88opGjBiRp1oWLFigQYMG6Z133tG7775bqME3AAAPOkJhAAAK0eHDh+Xh4aFHH31UISEhKlasWI7jMjIyVLZsWc2aNUt9+/bNtj8hIUFly5bV6tWr1aFDB0nSihUr1KtXL23ZsiWrR+PixYv13HPP6fXXX9eHH35YaBfE+/fvV2BgoAIDA3XixAm1bNlSffr0Ua9evVS2bNlCOQcAoGAuXLigwMBABQQEaNeuXXriiSfk7+8vX1/f2/rf6EOHDqldu3Zyc3PLuhv53yIiItSxY0dt375djRo1knT1DuZy5cpp6tSp6t+/f67zjxgxQvv27ctXv+BVq1apd+/e6t+/v2bNmpUVRAMAgNwRCgMAUEgOHTokDw8P1atXT8uXL1eRIkVuOHbLli1q2bKlYmNjb3gB37FjR1WvXl1ffPFF1jY/Pz9t375dv/76a9bF+Lp169SjRw8999xzmj17dqFeEFssFm3fvl2BgYFasmSJzp8/r3bt2snX11fdu3fPdocYAOD2SEtL05o1axQQEKDVq1erTJky6tevn/z8/OTm5nbbz79nzx516NBBzZo10+LFi7PdhZyamqrHH39cbdq0ue5za+vWrWrRooWio6NVrly5XM8RFRWlDh06KC4uTqVLl85zbZs3b1aXLl3k4eGhRYsWycHBIX8vDgAAA8pLKMxCcwAA3MT+/fvl7u6uRo0aKTg4ONdAWJLCwsLUsGHDXO/o6tGjh0JCQmQ2m7O2zZw5U0lJSRozZkzWto4dOyoiIkKLFy/Ws88+q7S0tFt/Qf8wmUxq0qSJZsyYoTNnzmjdunWqVKmSRo4cKWdnZ/Xs2VPLli1TcnJyoZ0TAHCVxWLRzp07NXz4cLm4uMjX11cODg5auXKlTp06pcmTJ9+RQHj79u1q06aN2rVrp6VLl+bYluLTTz9VfHy8Pvroo+u2h4aGqlGjRjcNhCWpZcuWKlGihFavXp2v+lq0aKGffvpJ27Ztk5eXly5dupSv4wEAQM4IhQEAyMXu3bvl7u6u1q1ba9myZXnq4bh27Vp5eXnlOsbHx0exsbH6+eefs7aVLFlS8+fP16xZsxQZGZm1vVmzZtq4caM2btwoHx8fJSYmFvwF3YC1tbU8PDz09ddfKzY2NisY8Pf3V7ly5eTn56c1a9YoPT290M8NAEZy7ty5rMC3UaNG+vXXXzVx4kTFxMQoMDBQnp6esrGxuSO1bNq0Se3atVPPnj317bffytbWNtuYU6dOacKECZo0aVK2O3xXr16tzp075+lctra28vb2VkhISL7rdHNz09atWxUdHS13d3fFxMTkew4AAHA92kcAAHADO3bsUMeOHeXt7a1vvvkmTxfpcXFxKleunLZu3aqmTZvmOrZ58+Zq2rSppk6det32YcOGadWqVdq/f78eeuihrO1Hjx5V+/btVaFCBYWGhl63IvztkpiYqFWrVikwMFBr165ViRIl9PTTT8vX11ctW7aUlRXfLwPAzSQlJSkkJEQBAQGKiIhQlSpV5OfnJz8/P7m6ut6VmtatW6fu3btr4MCBmj59+g3/e96zZ0+dPXtWW7duvW7MqVOnVLVqVe3evVsNGjTI0zmXLVum/v3768KFCwVqA3HhwgV5e3vrwoULWrdunWrUqJHvOQAAMALaRwAAUEBbtmxRu3bt1L17dwUEBOT5rq1169apVKlSaty48U3H9ujRQytWrNB/v3idPHmy7O3tNXLkyOu2u7q6avPmzbp06ZJat259R+6UKlasmPr06aOQkBDFxsZq0qRJOnLkiNq2basqVarotdde086dO7O9BgAwOovFop9++kkDBw5UhQoVNHjwYLm4uCgqKkpHjx7V+PHj71ogHBwcrK5du2rEiBGaMWPGDQPhsLAwBQcHa86cOdnGrF69Wi4uLqpfv36ez9uxY0elp6crKiqqQHWXKVNGUVFRevTRR9W8eXPt3r27QPMAAADuFAYAIJsff/xRXbp0yVrcLT93w/bt21eZmZkKCgq66dhjx47J1dVVe/bsUb169a7bt23bNrVo0ULLly+Xj4/PdfsuXryoTp066cKFC4qIiFD16tXzXF9hiYmJ0ZIlSxQYGKiff/5ZNWrUkK+vr3x9fVWrVq07Xg8A3CuOHz+uhQsXauHChTp+/LjatWsnf39/devWTcWKFbvb5SkwMFDPPfecxo8fr7fffvuG41JTU+Xm5qaOHTtq1qxZ2fZ37txZLi4umjt3br7O36lTJ1WuXFlffvllvmu/Jj09XQMGDFBwcLCCg4Pl4eFR4LkAAHgQcacwAAD5FB4erk6dOmngwIE53hmVm8zMTK1bt+6m/YSvefjhh/X4449rxYoV2fY1bdpUo0eP1uDBg3X+/Pnr9pUuXVrr169X9erV1bx5cx04cCDPNRaW8uXL6+WXX9a2bdt07NgxvfDCCwoJCVHt2rVVv359TZo0SSdPnrzjdQHA3fD3339r3rx5atWqlR5++GEFBQVp0KBBOnXqlMLDw9W3b997IhCeN2+e+vXrp6lTp+YaCEvS1KlTdenSJU2YMCHbvqSkJEVGRua5n/C/+fj4aOXKldcttJpftra2CggI0ODBg9WpUyctWbKkwHMBAGBU3CkMAMA/QkND1bNnT40cOVIff/yxTKZcv1jNZseOHXrqqacUHR2t8uXL5+mY8ePHa/ny5dq7d2+2fampqWrcuLFq1KihZcuWZasnNTVVffv21Y8//qg1a9boqaeeyle9t8PBgwcVGBiowMBAHT16VE2bNpWvr6969+6dp9XpAeB+kZmZqcjISAUEBGjFihUqUqSIfH195efnp0aNGuX7M+R2mzlzpl555RV9/vnnGjx4cK5jT5w4odq1a2vOnDnq379/tv2hoaF6+umnFR8fn++w+9y5c6pYsaK2bdumJk2a5OvYnEyZMkVjxozRzJkzNWzYsFueDwCABwF3CgMAkEfLly9Xjx49NGbMmAIFwpK0du1aNWjQIM+BsCR1795d+/bt09GjR7Pts7e318KFC7Vq1Sp99913Oe4PCgpS9+7d5eHhofXr1+e75sJWu3ZtTZgwQYcPH9aOHTvUpEkTffzxx3JxcVH79u01f/58JSQk3O0yAaDADh48qDfeeENVqlSRt7e3rly5okWLFuncuXOaNWuWGjdufM8Fwh9//LFeffXVrLtrb2bkyJFq0KCB/Pz8cty/evVqtWnTpkB3P7u4uOipp55SSEhIvo/NyahRozR//nyNGDFCY8eOpcc9AAB5RCgMADC8oKAg9e7dW++9957Gjx9f4Iv5sLAweXp65uuYunXrytXVNccWEpJUr149jR8/Xi+99JLOnDmTbb+NjY2++uorvfjii/L29tby5csLVHthM5lMatSokaZNm6ZTp04pMjJSrq6uGjVqlMqVK6du3bpp8eLFSkpKutulAsBNxcfHa9asWWrUqJHq1KmjiIgIjR49WmfPnlVISIh69Oghe3v7u11mNhaLRe+8847GjRunJUuWqF+/fjc9ZvXq1Vq1atUNe+pbLBaFhoYWqHXENT4+PoUWCkuSv7+/QkJC9Mknn2jo0KHKzMwstLkBAHhQ0T4CAGBoCxcu1PPPP6/JkyfrtddeK/A88fHxKlu2rDZu3KiWLVvm69hRo0Zp69at2rJlS477MzIy1LJlSxUvXlzr1q3LMbS2WCyaNGmS3nnnHc2dO1cDBgwo0Ou43dLT0xUREaHAwEAFBwfLYrHIx8dHvr6+6tChg+zs7O52iQAgSUpLS9PatWsVEBCg0NBQOTk5qV+/fvLz81PdunXvdnk3ZbFY9Oqrr+qLL77QDz/8oE6dOt30mJSUFNWpU0edO3fWjBkzchyzd+9e1a9fX8ePH1e1atUKVNvBgwdVp04d/fnnn3rkkUcKNEdOtm3bJm9vb7m7u+v777+Xg4NDoc0NAMD9hPYRAADk4quvvtLzzz+vmTNn3lIgLEkREREqXrx4gfojdu/eXVu3blV0dHSO+21sbLRw4UJt3rxZn3/+eY5jTCaTxowZo9mzZ2vQoEGaOnVqvuu4E2xtbdWpUyd9++23io2N1YIFC5ScnKynn35a5cuX1+DBgxUVFcVdXgDuCovFol27dunll19WxYoV9cwzz8jOzk4hISE6ffq0pkyZcl8EwpmZmRoyZIi++uorrVmzJk+BsCRNnjxZiYmJGj9+/A3HrF69WnXq1ClwICxJtWrVUo0aNQr1bmHp6iKtmzdv1i+//KKOHTvSrggAgFwQCgMADGn27NkaOnSovvjii0JZmGbt2rVq3769bG1t831skyZNVL58+Vwvjh955BFNnjxZo0aN0pEjR244bsiQIfr+++/11ltv6a233rqneysWLVpUvXr10vLlyxUbG6tp06bp5MmT6tChgypXrqyRI0dq+/bt9/RrAPBgOHfuXFbg27BhQ+3evVsfffSRYmJiFBQUJC8vL9nY2NztMvMkIyND/v7+WrJkiSIiItSmTZs8HXfs2DFNnDhRU6ZMUcmSJW847lZbR0hXv8j08fFRcHDwLc2Tk9q1a2vr1q06f/68WrdufcMvXAEAMDraRwAADGfatGlZC9P4+/vf8nxms1kuLi764IMPNHDgwALN8eKLL+ro0aMKDw/P9TwdO3ZUYmKifvrpJ1lbW99wbFhYmHr06CE/Pz/Nnj0717H3mri4OC1dulSBgYHasmWLHn74YfXp00e+vr5yc3O72+UBeEAkJycrODhYAQEBioiIUOXKleXn5yc/Pz/VqFHjbpdXIGlpafL19dWmTZsUHh6uBg0a5PnYLl26KCEhQZs2bbphb/3z58+rXLlyBWqV9F8//fSTWrdurZiYGDk7O9/SXDmJj49X586dFRMTo/Dw8EJtUwEAwL2O9hEAAPzHRx99pNGjR+u7774rlEBYkvbs2aPY2Nh8LzL3b927d9ePP/6ov/7664ZjrKysNH/+fB08eFBTpkzJdT5PT09FRERo8eLF6tu3r9LS0gpc253m7OysYcOGafPmzTp58qSGDh2qtWvXqm7duqpbt64++ugjHTt27G6XCeA+ZLFYtHnzZg0aNCirZY2Li4siIyN17Ngxvf/++/dtIJycnKxu3bpp27Zt2rhxY74C4VWrVmnt2rWaM2dOrouthoWFqWTJkmratOkt19usWTM5OTkpNDT0lufKiZOTk9avX6/atWurefPm2rlz5205DwAA9ytCYQCAIVgsFr377rt67733tGTJEvXp06fQ5g4LC5Obm5sqVapU4Dnc3d3l6Oio1atX5zqucuXKmjlzpsaNG6d9+/blOrZ58+bauHGjNmzYIB8fHyUlJRW4vrulSpUqGjVqlHbv3q1Dhw6pZ8+eCggIkKurq5o0aaIZM2bwaDCAmzp+/LjGjx+vGjVqqFWrVjpx4oRmz56tmJgYzZ8/X+7u7rKyun8vjS5fvqxOnTrpwIED2rRpk2rXrp3nY5OTk/Xyyy/r5Zdfvmm/5NDQ0EJrpWFtba0uXboUel/hfytWrJiCg4Pl5eWlNm3aKCIi4radCwCA+w3tIwAADzyLxaI333xTn376qX744Ydb7oX4Xy1btlTTpk01efLkW5rnueeeU2JiopYvX57rOIvFoh49euj48ePasWOH7Ozsch1/5MgRtW/fXi4uLgoNDVWpUqVuqc67zWKx6Ndff1VgYKCCgoJ09uxZubu7y9fXVz179lTp0qXvdokA7gF///23li1bpoCAAG3atEk1a9aUv7+/+vXrp8qVK9/t8gpNQkKCvLy8dOHCBa1fv15Vq1bN1/Hvvvuu5s6dqz/++EMlSpS44bj09HSVKVNGn3/+uZ599tlbLVuSFBISIl9fX124cEFFixYtlDlzYrFY9MYbb2j69OlauHBhoX4xDADAvSgv7SMIhQEADzSLxaJXXnlFX375pUJCQtShQ4dCnT8hIUFlypRReHi42rZte0tzrVixQn379s3TxXFcXJzc3Nw0aNAgffjhhzed++zZs+rYsaOsra21bt06lS9f/pZqvVeYzWZt2bJFgYGBWrp0qS5duqSOHTvK19dXXbt2laOj490uEcAdlJmZqaioKAUEBGj58uVycHCQr6+v/P391ahRo1xbI9yPzp8/rw4dOigtLU3r169XhQoV8nX8kSNH5Obmpvnz59806N2wYYM8PDx0/vz5QvvyLSkpSWXKlFFgYKB8fHwKZc7cfPLJJxo9erQ+/fRTvfzyy7f9fAAA3C2EwgAAQzObzRo2bJgWLlyo0NDQPK/Anh/Lli1T//79dfHixZvesXsz1y6Ov//+e3Xr1u2m44ODg9WzZ09t2bJFTZo0uen4+Ph4derUSfHx8YqIiFD16tVvqd57TXp6uiIjIxUUFKTly5crIyNDXbt2la+vrzw9PWVvb3+3SwRwmxw6dEgBAQFatGiRYmNj5eXlJX9/f3Xu3PmB/bd/7tw5tW/fXvb29goPD1eZMmXydbzFYpG3t7eSkpL0448/3jQwf/3117Vjxw5t2rTpVsrOxsfHR05OTpo/f36hznsjixYt0vPPP6/Ro0frgw8+eOC+KAAAQGKhOQCAgWVmZmrQoEH67rvvtG7dutsSCEtX+wl7eHjcciAsSUWLFlXHjh1v2j7imm7duqlfv37y8/NTYmLiTcc7OTkpMjJS1apVU4sWLXTgwIFbLfmeYmtrK09PT33zzTeKi4vTokWLlJmZqT59+qhcuXIaMGCAIiIilJGRcbdLBVAI4uPjNXv2bDVu3Fi1a9dWeHi4Ro0apbNnz2rlypXq2bPnAxsInzx5Uq1atdJDDz2kqKiofAfC0tXWDREREZo9e3aegtHQ0FB5e3sXpNxc+fj4aNWqVcrMzCz0uXPSr18/rVy5UtOnT9egQYP4TAAAGBahMADggZORkSF/f38tX75c69evV4sWLW7LeSwWi8LCwuTl5VVoc/bo0UOrVq1Senp6nsbPmDFDycnJGjNmTJ7GX1vMrkmTJmrVqpW2b99+K+XesxwcHNSjRw8tXbpUsbGx+uyzzxQTEyMvLy9VrFhRw4cP19atW8WTUMD9JT09XSEhIerRo4cqVKigDz74QK1atdK+ffu0e/dujRgxQs7Ozne7zNvq8OHDatmypapUqaLw8HCVLFky33MkJSVpxIgRGjlypOrUqXPT8UeOHNEff/xR6D35Jalz586Kj4/X1q1bC33uG/Hy8lJUVJRWrFihnj17Kjk5+Y6dGwCAewWhMADggZKenq5nn31Wa9euVWRkpBo3bnzbzvXbb7/p7Nmz8vT0LLQ5O3furCtXrmjDhg15Gl+yZEktWLBAs2fPVmRkZJ6Osbe31+LFi9WtWzd5eHho/fr1t1Dxva9EiRJ67rnntGbNGsXExGj8+PHat2+fWrRooerVq2vMmDHau3cvATFwj7JYLFmBr4uLi5555hnZ2dkpJCREp0+f1tSpU1W3bt27XeYdceDAAbVq1Up169bV6tWrC9w3/cMPP1RGRobGjRuXp/GrV69WtWrVVLt27QKdLzfOzs5q3ry5QkJCCn3u3Dz11FPavHmzfv31V3Xo0EF//fXXHT0/AAB3G6EwAOCBkZqaql69emnDhg3asGGDnnjiidt6vrVr1+qxxx5TtWrVCm3OUqVKyd3dXStWrMjzMe3atdOwYcPUv39/JSQk5OkYGxsbff3113rxxRfl7e2d55YV97syZcpo6NCh2rhxo06dOqXhw4dr/fr1ql+/vurUqaMJEyboyJEjd7tMAJKio6M1ZcoUPf7443ryySe1c+dOffjhh4qJiVFQUJC8vLxkY2Nzt8u8Y3bv3q3WrVurefPmWrFihYoUKVKgef78809NmTJF06ZNU/HixfN0TGhoqDp37nzb+u/6+PgoJCTkjn85V6tWLW3dulUXL15Uq1atdO7cuTt6fgAA7iYWmgMAPBCSk5PVs2dP7dmzR5GRkapVq9ZtP2fbtm1Vr149ffrpp4U67+eff64JEybozJkzsrLK2/e3SUlJql+/vpo2baqAgIA8n8tisWjSpEl65513NHfuXA0YMKCgZd/XDh8+rKCgIAUGBurQoUNq2LCh+vTpo2eeeUaVKlW62+UBhpGcnKyQkBAFBAQoPDxclStXlp+fn/z8/FSjRo27Xd5ds3XrVnl5ealr165asGBBgcNwi8UiT09PZWRkaP369XkKeS9fviwnJyetXLmyUJ+M+bfDhw/r0Ucf1YEDB27L3cg3c/HiRXXp0kVnz57VunXrVLNmzTteAwAAhYmF5gAAhpCYmKguXbpo//792rhx4x0JhC9fvqzNmzcXaj/ha3x8fBQdHZ2vfr9FixbVwoULtWjRIgUHB+f5OJPJpDFjxmj27NkaNGiQPvnkk4KUfN975JFHNHbsWB04cEB79uyRh4eHPvvsM1WpUkWtW7fWF198oQsXLtztMoEHksVi0ZYtWzR48GBVqFBBgwYNUvny5RUZGaljx47p/fffN3QgHBUVpQ4dOsjX11cBAQG3dHf08uXL9eOPP+Z5cTlJioiIkK2trdzd3Qt83pt55JFHVKtWrTveQuKa0qVLKyIiQv/P3n0H1ny3/x9/hhCjajSLxKraI6hWiJVEZIgMM8dItJQWrRqltMaNVosaRVEzCU4kEmTvRsSmQhVBKwhZSGq02ef3R7/87u/37t0a55xPEtfjP+S8rxeSnJPrvN/Xu2PHjtjY2HDixAlFcgghhBD6JE1hIYQQFdqDBw9wcXHhl19+ITk5mZYtW+qlbmJiIoaGhvTp00frazdq1IgePXo80wgJAGtra2bPns2ECRPIycl5psdOnDiR3bt38+mnn/LZZ5+9tPN1DQwMsLKy4quvvuLatWukpKTQqVMnFixYQMOGDXFxccHf358HDx4oHVWICi89PZ1FixbRsmVLevfuzbVr155cCrl9+3b69ev31KclKqvIyEhcXFyYMGECGzZseKF/j0ePHvHxxx8zffp02rRp89SPCw8Px8HBgRo1ajx37afh7u7+TG9qalutWrXYt28fbm5u2NnZERMTo1gWIYQQQh9e7ldZQgghKrT8/HwGDBjA7du3SU5Opnnz5nqrHRUVha2trc5+SPb09GTfvn3P3JxduHAhjRo14v3333/mx44YMYKwsDBWrVrFpEmTKC0tfabHVzYGBgb07NmTtWvXcuvWLaKiomjYsCEffvghpqamDBs2jJCQEAoKCpSOKkSF8eDBgycN3+bNm7N7927GjRvH9evXiYuLY8yYMdSuXVvpmOVCcHAwHh4ezJo1i2+++eaF5/kuXrwYgM8///ypH1NWVkZkZCQDBw58odpPw93dnRMnTig619fQ0JCtW7fy4Ycf4urqyq5duxTLIoQQQuiaNIWFEEJUSPfu3aN///7k5+eTnJxM48aN9VZbo9EQHR2ts9mK8GdT+OrVq5w/f/6ZHle9enX8/PwIDw9n586dz1zXycmJuLg41Go1o0aNoqio6JnXqIwMDQ3p378/W7duJTs7m4CAAKpWrcro0aMxMzNj7NixxMTEUFJSonRUIcqd0tJS4uLinny9zJgxg3bt2nHs2DEuXrzInDlz9Po9vCLw9/dnxIgRLF68mEWLFr1wQ/jixYt88803rFq1ildeeeWpH3f69Gmys7NxcXF5ofpP4+2338bc3JywsDCd1/o7BgYGLF26lOXLl+Pt7c3q1asVzSOEEELoilw0J4QQosLJzc3FwcGBsrIy4uPjMTU11Wv9ixcv0q5dO65cuaLTOZedOnVi6NChzJ8//5kf+9VXX7F06VLOnz//XM2Ws2fP4ujoSJcuXQgODqZWrVrPvMbL4OHDh4SGhqJWq4mJiaFu3boMGzYMlUqFjY3NS3/0XbzcLl26hK+vLzt37iQzMxMXFxd8fHxwdXXFyMhI6Xjl1qZNm5g0aRKrV6/mww8/fOH1NBoNDg4OVKlShZiYmGdqMC9YsIDw8HBOnz79wjmexsSJE7l58yaRkZF6qfdPdu/ejY+PDzNmzGDp0qUv3JwXQggh9OVpLpqTprAQQogKJSsrC3t7e2rUqEFsbCyvvfaa3jOsWrWK9evXc/XqVZ3WWbBgAQcOHCA1NfWZH1taWkrv3r2pXbs2MTExz9WcvHr1Kg4ODlhYWBAeHk69evWeeY2Xyb179wgODkatVpOUlISlpSUjRoxApVLRpUsXaSaIl8Ldu3cJCAjAz8+PEydO0LlzZ3x8fBg5cqTe38CriFatWsXMmTPZvHkz7777rlbWDAwMZPTo0Zw/f55WrVo9XDSdLAAAIABJREFU02O7deuGi4sLixYt0kqWfxIZGYmnpyd37tyhTp06eqn5T2JjYxk8eDDDhg1j8+bNL3TRnxBCCKEv0hQWQghRqdy6dQs7Ozvq1atHTEyMYk3KAQMG0Lp1a9auXavTOqmpqXTp0oVff/31ueYlX7lyhc6dO7Ns2TImT578XBlu3brFgAEDqFatGtHR0Zibmz/XOi+b27dvExQUhFqt5vjx47Rq1QqVSoVKpaJ169ZKxxNCq4qLi4mKisLX15ewsDAaNGjA6NGj8fb2plOnTkrHqxA0Gg1Llixh0aJF+Pv74+XlpZV1Hzx4QJs2bRg7dixffPHFMz329u3bWFhYcPz4cd5++22t5PknBQUFGBsbs2PHDoYOHaqXmk/jxIkTuLi40KNHD/bs2SOnZ4QQQpR7T9MUljONQgghKoTr16/Tp08fTExMiIuLU6wh/OjRIw4ePKjTecKPWVlZ0bx5c/bt2/dcj2/ZsiXLly/nk08+4cqVK8+1hoWFBcnJyRgZGdGrVy+uXbv2XOu8bBo1asTUqVM5duwYv/zyCz4+PgQHB9OmTRu6du3K8uXLuXHjhtIxhXhuGo2GM2fO8PHHH2NhYcHw4cMxNDRk//79ZGRksGLFCmkIPyWNRsOcOXNYsmQJe/fu1VpDGGDRokUYGhoyd+7cZ35sZGQkpqamdOvWTWt5/kmNGjVwcnLiwIEDeqv5NN5++20OHz7MuXPnGDBgAPfu3VM6khBCCPHCZKewEEKIcu+XX37Bzs6OFi1aEBoa+kyX5GhbREQEQ4YM4e7du9SuXVvn9WbMmMHx48dJSUl5rsdrNBocHR158OABhw4deu5jrw8fPsTDw4OLFy8SGxtL+/btn2udl9358+dRq9Wo1WquXbuGjY0NKpWKYcOGydF6USFkZmaya9cufH19OX/+PD179sTb25vhw4dTv359peNVOGVlZUydOpWtW7eyf/9+BgwYoLW1f/75Zzp37kxgYCCenp7P/HhPT0/q1avH9u3btZbpafj7+/PRRx+Rk5NDtWrV9Fr7n9y6dQsnJ6cnF85aWloqHUkIIYT4SzI+QgghRIWXlpaGnZ0dHTp0YN++fYof2fzwww9JS0sjNjZWL/UOHz5M7969yczMxMzM7LnWyMjIoEOHDsyePZs5c+Y8d5bCwkJGjhxJUlISUVFRejtOXBlpNBpOnDiBWq1mz5495ObmYm9vj0qlwtPTk7p16yodUYgnCgoKOHDgAL6+vsTExGBpaYm3tzfe3t60bNlS6XgVVmlpKRMmTCAoKIiIiAh69+6ttbU1Gg12dnbUqFGDyMjIZ55p/niMg6+vL0OGDNFarqdx7949TE1NiY2Nxc7OTq+1n0ZeXh5ubm5cv36dmJgY2rZtq3QkIYQQ4j/I+AghhBAV2s8//0zfvn3p2rUrBw4cULwhDBAVFYWzs7Pe6vXo0QNTU9MXOkpraWnJ2rVrWbBgAWfPnn3udYyMjNizZw8eHh7Y2dkRHx//3Gu97AwMDOjevTurV68mIyOD2NhYmjRpwvTp0zEzM2Pw4MEEBQXxxx9/KB1VvKQ0Gg2HDx9mwoQJmJubM27cOExNTYmPj+fatWssXrxYGsIvoLi4mNGjRxMSEkJ8fLxWG8IAAQEBHDlyhLVr1z7XJZcHDx6kqKgIBwcHreZ6Gg0aNKBPnz7lboTEY/Xr1yc2NpYuXbrQq1cvjh8/rnQkIYQQ4rlIU1gIIUS5lJqaSr9+/bCxsSE4OJgaNWooHYmrV6/yyy+/6GWe8GNVqlTB3d2dkJCQF1pn9OjRDBo0CG9vbwoLC597HUNDQ7Zs2cL777/PwIEDXziXgKpVq2JnZ8fmzZvJyspi79691KxZk3feeQdTU1NGjx5NREQExcXFSkcVL4H09HQWL15Mq1at6N27N7/++itr164lOzubHTt2YGtrS5Uq8iPEiygoKGDo0KEkJCSQlJSk9VMX9+/fZ8aMGcyePZs33njjudaIiIigT58+vPrqq1rN9rTc3d05cOAA5fW0as2aNQkODsbT0xM7OzuioqKUjiSEEEI8M3lFJ4QQotw5deoUdnZ29O/fnz179lC9enWlIwF/7hJu2rQpbdq00WvdwYMHk5iYSH5+/nOvYWBgwMaNG8nKymLhwoUvlMfAwIDly5ezcOFChg8fzrZt215oPfH/Va9eHVdXV3bt2kV2djZbtmzh4cOHDB48GHNzcyZOnEhSUhJlZWVKRxWVyIMHD9i+fTu2trY0b96cnTt38s4775Cenk58fDxjxozRywz1l8Hvv/+Om5sbp06dIjk5GSsrK63XWLhwIUZGRnz66afP9XiNRkN4eDiurq5aTvb03N3duX79OufOnVMswz8xNDRk8+bNTJs2DTc3N/z9/ZWOJIQQQjwTaQoLIYQoV44ePYq9vT1ubm7s3LnzuS9G04Xo6GicnJye6yjui7C1taVWrVpERES80DomJiZ8//33LFu2jCNHjrzQWgYGBsyZM4d169bx3nvv8c0337zQeuI/1a5dmxEjRrB//36ys7NZvnw5v/76K/b29jRu3Jjp06dz8uTJcruTTpRvpaWlTxq+5ubmzJgxg7Zt23Ls2DEuXbrE3LlzadKkidIxK5X79+/j5OTElStXOHTokE7eYPzpp5/49ttvWbNmzXOPXLp48SLXrl1TtCncrFkzOnXqVG5HSDxmYGDAkiVLWLlyJWPHjpXnQiGEEBWKXDQnhBCi3Dh48CADBw5k5MiRbNy4sVwdUS4oKKBBgwao1Wrc3d31Xn/UqFEUFhayd+/eF15r7NixHD58mNTUVK3s/tuzZw+jR49m1qxZLFmyRO9N85dNVlYWQUFBqNVqjh49yhtvvIGXlxcqlYp27dopHU+Uc5cuXcLX15edO3eSmZmJs7MzPj4+DBo0CCMjI6XjVVr37t3DycmJ3377jYSEBCwtLbVeQ6PR0LdvX1599VXCwsKe+3vx8uXL2bJlC2lpaVpO+Gzmz59PeHg4P/74o6I5nlZAQADe3t5MnTqVr7/+uly9hhFCCPHyeZqL5qQpLIQQolyIj4/Hzc2NcePG8e2335a7xmJsbCyurq7cvXuXOnXq6L1+cHAw3t7e3Llzh5o1a77QWr/99hsdO3bEzc2NdevWaSVfVFQUQ4YMwcfHh3Xr1lG1alWtrCv+Xnp6Onv27EGtVnP27Fk6deqESqVixIgRNG/eXOl4opy4d+8eAQEB+Pr6cuLECTp37oy3tzcjR47EzMxM6XiVXk5ODg4ODmg0GuLi4nT2b75z507Gjx/PhQsXeP311597nb59+9KtWzfFd72ePn2abt26cf369Qqzaz0+Ph5PT08GDx7Mli1bqFatmtKRhBBCvKSkKSyEEKJCiIyMZPDgwUyZMoXly5eXu4YwwLRp0zh79iyJiYmK1H/06BHGxsYEBARoZadyYmIi/fv3JyYmRmu3yx8+fJiBAwfi5OSEn59fuZkF/bK4cOECAQEBqNVqrl69irW1NSqViuHDh2Nubq50PKFnxcXFREVF4efnR1hYGPXr12fUqFH4+PjQqVMnpeO9NG7duoW9vT116tQhJiaGBg0a6KROfn4+bdq04YMPPmDBggXPvU5eXh4mJibExcVha2urxYTPTqPR0KRJE2bPns2UKVMUzfIsTp06hYuLC2+99RaBgYEyj1sIIYQinqYpLGdahBBCKOrAgQN4eHgwc+bMctsQhv8/T1gptWvXxtHRkX379mllPTs7O6ZMmcI777zzQhfY/TsbGxsOHjxIUlISHh4e/P7771pZVzyddu3asWjRIi5fvszJkyfp2bMny5Ytw8LCgv79+7N161by8vKUjil0SKPRcObMGT7++GMsLCwYNmwYVapUISQkhIyMDL755htpCOvRtWvX6N27NyYmJsTHx+usIQywYMECateuzaxZs15onZiYGGrXrk2vXr20lOz5GRgY4O7uXu7nCv9f3bp1IyUlhQsXLtC/f3/u3r2rdCQhhBDiL8lOYSGEEIoJCgpi5MiRzJ8/n3nz5ikd579KT0+nefPmnDt3jo4dOyqWw9fXl+nTp5OVlaWVI6m///47Xbp0oXv37vj5+Wkh4Z+uXr2Kg4MDFhYWhIeHU69ePa2tLZ5NWVkZKSkpqNVqgoKCuH//Ps7OzqhUKgYNGiQ72CqJzMxMdu/eja+vLz/99BM9evTAx8eH4cOHU79+faXjvZTS0tKwt7enbdu27N+/X6dfa2fPnqVr166EhoYycODAF1pr9OjRFBUVERgYqKV0LyYuLg4XFxdyc3Mr3HPJ7du3cXZ2pri4mJiYGBo3bqx0JCGEEC8R2SkshBCi3Nq5cycqlYovvviiXDeE4c9dwhYWFnTo0EHRHIMGDeK3334jOTlZK+vVqlULX19fdu3apbUdyABvvPEGKSkp5OXl0a9fP7Kzs7W2tng2VapUoU+fPmzYsIHMzEwOHDhA3bp1ee+99zA1NWXkyJGEhYVRVFSkdFTxjAoKCtizZw8uLi5YWlry7bff4ubmRlpaGkeOHGHixInSEFbIuXPn6NOnD127diUsLEynDeGysjImT57MoEGDXrghXFpaSlRUFK6urlpK9+L69u1LrVq1iIqKUjrKM2vUqBEHDx7ExMSEnj17cuHCBaUjCSGEEP+LNIWFEELo3bZt2/Dx8WHlypUvfNRVH6KionByclJ8tEWDBg3o16+fVhu41tbWzJkzh4kTJ5KTk6O1dS0sLEhOTsbIyIhevXqRnp6utbXF86lWrRrOzs74+fmRk5ODr68vhYWFDBs2DDMzM8aPH09CQgKlpaVKRxX/hUajedLwNTc3Z9y4cZiamhIfH8+1a9dYsmQJrVq1UjrmS+3kyZP069cPW1tbgoODqVGjhk7r+fv7c/r0aVavXv3Cax07doy8vDycnZ21kEw7qlevjouLS4UbIfFYvXr1iImJ4a233qJXr14cPXpU6UhCCCHEE9IUFkIIoVcbNmzgvffe47vvvuOjjz5SOs4/KioqIiEhQdF5wv/O09OTffv2UVZWprU158+fj4WFBRMmTECb46Jee+014uPjadq0KTY2NrJLqhypWbMmQ4cOJTg4mOzsbFavXk1GRgaOjo5YWloydepUjh07ptXPB/H8rl+/zuLFi2nVqhW9evXi6tWrfPvtt2RlZbFjxw5sbW2pUkVe1istJSUFe3t73N3d2bVrl1bG/PydvLw8PvnkEz777DOaNWv2wuuFh4fTvXt3TExMXjycFrm7uxMZGUlhYaHSUZ5LjRo1CAoKYtiwYdjb2xMREaF0JCGEEAKQprAQQgg9Wr16NVOmTGHLli1MnDhR6ThPJSUlhYKCAvr37690FAA8PDy4ffs2J0+e1Nqa1atXx8/Pj6ioKK3OFgaoU6cOERERdO/end69e3PixAmtri9eXN26dfHx8SE6OprMzEzmzZvHmTNn6NGjBy1atGDu3Ln89NNPSsd86Tx48OBJw7dZs2b4+/szduxY0tPTSUhIwNvbm1deeUXpmOJ/xMXFMWDAALy9vdm6dStVq1bVec158+ZRt25dZs6cqZX1wsPDy9XoiMecnZ0pKCggKSlJ6SjPrWrVqmzcuJGZM2fi7u6Or6+v0pGEEEIIaQoLIYTQj6+//pqZM2fi7+/PO++8o3ScpxYdHU2PHj3KzQU3FhYWdO/eXasjJAA6duzIokWL+Oijj7hx44ZW1zYyMiIwMBB3d3fs7OxISEjQ6vpCe0xMTJg0aRLJycncuHGDSZMmERMTQ6dOnejQoQNffPEFv/zyi9IxK63S0lLi4+Px9vbG3NycadOm0aZNG44ePUpaWhqfffYZTZo0UTqm+D/CwsJwdXVlypQprF27Vi+7tn/88Uc2bNjA2rVrtTKi4vr165w/f/6F5xLrQt26dbG1ta2wIyQeMzAwYNGiRaxZs4Z3332XZcuWyWkMIYQQipKmsBBCCJ3SaDQsWrSIzz//nICAAEaOHKl0pGcSFRVVruYrwp8jJEJCQrT+w+TMmTPp0KED7777rlbHUwAYGhqydetW3n//fVxcXLTe1Bba17hxY2bOnMnp06e5dOkSw4YNw9/fnzfeeIPu3buzevVqbt++rXTMSiEtLY25c+fSrFkznJycuHfvHjt27CAzM5MNGzZgbW2t+Exz8df27NnD4MGD+eyzz/j666/18v/0+HI5Dw8PrY02ioiIwMLCAisrK62sp23u7u6EhoZWiibq5MmTCQgIYN68ecycOVPrz7dCCCHE0zJ4midWAwMDTWV4AhZCCKFfGo2Gzz//nBUrVhAUFISbm5vSkZ5JRkYGjRs35vTp03Tt2lXpOE9cvnyZ1q1bc/78edq3b6/Vta9evYqVlRVff/01U6ZM0era8OfnxNKlS5k/fz6bN2+uULvGxZ//f6mpqajVagICAsjIyKBv376oVCqGDBnCa6+9pnTECuPevXvs2bMHX19fjh8/jpWVFT4+PowcORIzMzOl44mnsH37dsaPH8+yZcuYMWOG3upu27aNKVOmcOnSJa3tHHdxcaFJkyZs3LhRK+tp2+Pn45MnT9KtWzel42hFYmIiHh4euLm5sW3bNqpXr650JCGEEJWIgYEBGo3mb9+tlp3CQgghdEKj0TBz5kxWrlzJgQMHKlxDGP4cHWFmZkbnzp2VjvK/tGrVivbt2+tkt+0bb7zBihUrmDVrFpcvX9b6+gYGBsydO5e1a9cyfvx4Vq5cqfUaQncMDAzo0qULy5YtIz09neTkZNq1a8fnn3+Oubk5rq6u7Nq1i4cPHyodtVwqLi4mLCyMoUOH0rBhQxYuXEjPnj1JTU0lNTWVadOmSUO4gli/fj3jx49n/fr1em0I37t3j9mzZzNv3jytNYQfPXpEYmJiuRwd8ZilpSVvvvlmhR8h8e/s7OxISkoiLi4ONzc3Hj16pHQkIYQQLxnZKSyEEELrysrK+Oijj9i2bRthYWHY29srHem5DB06lNq1a5fLC2HmzZtHREQEP/74o9bX1mg0ODk58dtvv5GSkoKhoaHWawAEBAQwZswYZs+ezeLFi+V4fAVWUlJCYmIiarWakJAQiouLGTRoEF5eXjg7O2tl5mlFlpqaiq+vL7t37yY/Px93d3d8fHwYMGAA1apVUzqeeEbLly9nzpw5bNu2DW9vb73W/uCDD0hMTOTcuXMYGRlpZc2wsDCGDx/O3bt3qVWrllbW1IXFixcTGBhY6S6+vHr1Ko6OjhgbGxMREYGxsbHSkYQQQlQCT7NTWJrCQgghtKqsrIyJEycSEBBAREQEffr0UTrScykuLsbY2JhNmzbh5eWldJz/cObMGbp27cq1a9do1qyZ1tfPyMigQ4cOzJo1i7lz52p9/ceioqIYMmQIY8eOZd26dXq5oEnoVkFBAVFRUajVasLCwjAyMsLT0xOVSoWdnZ3O3mQob7Kysti1axd+fn6cO3cOa2trfHx8GDFiBPXr11c6nngOGo2GhQsX8uWXX7J7926GDRum1/qnTp3i7bffJiYmBgcHB62tO3HiRDIyMoiIiNDamrpw7tw5rKys+OWXX3j99deVjqNVWVlZODk5UVBQQExMDE2bNlU6khBCiApOxkcIIYTQq5KSEsaOHUtQUBBxcXEVtiEMcOzYMR4+fKjVH7y1qXPnzjRt2lRnF7ZZWlqyfv16Fi5cSGpqqk5qADg7OxMbG8vu3bsZNWoURUVFOqsl9KNGjRp4enoSGBhITk4O69atIycnh4EDB2JhYcGUKVM4fPhwpbxcqaCggMDAQAYOHIilpSVr1qzB1dWVS5cucfToUd5//31pCFdQGo2GTz75hK+//pp9+/bpvSFcVlbGpEmTGDp0qFaflzQaDREREeV6dMRjHTt2pHnz5pVqhMRj5ubmHDx4kIYNG2JjY8P58+eVjiSEEOIlIDuFhRBCaEVxcTFjxowhNjaWuLg43nzzTaUjvZC5c+fyww8/cPToUaWj/FfTpk3j9OnTJCcn62R9jUbDsGHDSEtL49SpU1o7qvxXzp49i6OjI127dmXv3r3l+gizeD537twhODgYtVpNcnIyjRs3xsvLC5VKhZWVVYUdH6LRaDh69Ch+fn7s2bOH4uJihgwZgo+PD/369ZPd75VAWVkZkydPxs/Pj9DQUEVGIm3evJlp06Zx6dIlLC0ttbZuamoqXbp0IT09vULsTp02bRpnzpwhKSlJ6Sg6UVBQwOjRo0lISCAsLIxevXopHUkIIUQFJTuFhRBC6EVRUREjRowgISGBH374ocI3hOHPS+acnJyUjvG3Bg8eTEpKCtnZ2TpZ38DAgA0bNpCTk8OCBQt0UuMxKysrDh06xIULFxgwYAD5+fk6rSf0z9jYmIkTJ5KUlMTNmzeZOnUqP/zwA126dKFdu3YsWrSIK1euKB3zqV2/fp0lS5bQunVrbGxsuHz5MqtXryYrKwtfX1/s7OykIVwJPD4Bs3v3bmJjYxVpCN+5c4dPP/2U+fPna7UhDBAeHk7Hjh0rREMYwN3dnUOHDnH37l2lo+hEjRo12LNnD15eXjg4OBAWFqZ0JCGEEJWYvFIVQgjxQgoKChg8eDBHjx7l4MGDWFlZKR3phWVlZXHmzBmcnZ2VjvK3evbsibGxMaGhoTqrYWJiwubNm1m+fDlHjhzRWR2Ali1bcvjwYfLy8ujXr5/Omt1CeRYWFkyfPp0TJ05w5coVRo0axZ49e2jVqhVvvvkmK1as4ObNm0rH/A8PHz580vBt1qwZfn5++Pj4kJ6eTmJiIj4+PrzyyitKxxRaUlRUhEqlIiIigsTERGxsbBTJMXfuXExNTfn444+1vnZ4eHiFGB3xWK9evahXr165n3/8IqpWrcp3333Hp59+iqenJ9u2bVM6khBCiEpKxkcIIYR4br///jseHh5cuHCBxMREWrVqpXQkrfD19WXGjBlkZ2dTtWpVpeP8rQkTJpCRkUFkZKRO67z77rskJydz9uxZateurdNad+/excXFhXv37hEXF6eTi/RE+aPRaPjpp59Qq9UEBASQnp5O7969UalUDB06FBMTE0VylZWV8cMPP+Dr60twcDDVq1dnxIgR+Pj4YG1tXWHHXoi/V1BQwNChQzl16hTx8fF06NBBkRzHjx+nR48exMfHY2dnp9W1c3JyMDc359ChQ4o1vJ+Ht7c3jx49Ijg4WOkoOrdx40YmT57MkiVL+PTTT+X7jRBCiKf2NOMjpCkshBDiuTx8+JBBgwZx7do1EhMTK9VN4F5eXlStWpVdu3YpHeUfRUVF4eHhQU5ODnXr1tVZnfv379OxY0dcXV1Zv369zuo89uDBAzw8PLh06RJxcXG0a9dO5zVF+aHRaDh27BhqtZrAwEDu3LmDg4MDKpUKDw8PXn31VZ1nSEtLw8/PD39/f27fvo2TkxPe3t64ublRo0YNndcXynn48CHu7u5cvnyZhIQExd7wLC0tpXv37rRs2RK1Wq319X19fZk+fTo5OTnl/g3QfxccHIy3tzd37tyhZs2aSsfRueDgYEaOHMkHH3zAypUrZSyNEEKIpyIzhYUQQujEb7/9hqOjIzdv3uTgwYOVqiFcWlpKbGxsuZ8n/JidnR1GRkY63yn86quvsmPHDjZs2EBsbKxOawHUqVOHiIgIunfvTu/evTlx4oTOa4ryw8DAgB49evDtt99y69YtYmJisLCwYOrUqZiZmTF06FCCg4P5448/tFo3Ly+PDRs20KNHD9q0aUN4eDgff/wxGRkZhIeHM3z4cGkIV3KPn9/S09M5dOiQoidgvv/+e9LS0lixYoVO1g8PD8fZ2blCNYQBHB0dKS0tJSEhQekoejFkyBBiYmLYvn07o0ePpqioSOlIQgghKgnZKSyEEOKZ5OXl4ejoyP3790lISMDCwkLpSFp19OhRevbsSXZ2NqampkrHeSojR46kpKSEwMBAndeaOnUqwcHB/PTTT9SvX1/n9UpKSpgwYQKBgYEcOHBAkUueRPlRWFhITEwMarWa0NBQqlatiqenJ15eXvTv359q1ao985rFxcXExMTg6+tLaGgodevWZdSoUfj4+NC5c2cd/C1EeXXnzh0cHR35448/iI+Pp1GjRoplyc3NpVWrVnz++efMmDFD6+sXFRVhYmLCpk2b8PLy0vr6uubq6krDhg3ZvHmz0lH0JjU1FScnJzp16kRwcDB16tRROpIQQohyTHYKCyGE0Ko7d+5gb2/P77//TlJSUqVrCANER0fz5ptvVpiGMICnpyeRkZFa3zX5V5YuXcorr7zCRx99pPNaAIaGhmzdupWJEyfi4uLCvn379FJXlE9GRka4ubmhVqvJyclh06ZN5OXl4e7uTqNGjfjggw9ITk6mrKzsH9dKTU1l+vTpWFpaMmTIEODPY9q3bt1i1apV0hB+yWRlZdGvXz/Kyso4ePCgog1hgE8//RQLCwudfa9NSUnh0aNHODo66mR9XXN3dycsLOypvtYri86dO3PkyBF+/fVX7OzsyM3NVTqSEEKICk6awkIIIZ5KdnY2tra2aDQakpKSMDc3VzqSTkRFReHs7Kx0jGfi7OxMSUkJ8fHxOq9Vq1Yt/Pz8UKvVervkx8DAgBUrVrBgwQKGDRvG9u3b9VJXlG+1a9dGpVIRGhpKVlYWS5cu5fLly/Tr14+mTZsyc+ZMTp8+zb+fdsvOzmblypVYWVnRpUsXjhw5wsKFC8nMzCQoKAhXV9fn2m0sKrYbN27Qu3dvXnnlFRITExW71PCxo0ePsn37dtavX6+zz8fw8HBsbGz0cuJDFwYNGkROTg7Hjx9XOopevf766xw+fJjS0lJ69epFenq60pGEEEJUYDI+QgghxD+6ffs29vb2vPLKK8TExNCgQQOlI+lEbm4uZmZmFe4mdgA3NzeMjY3Ztm2bXurNmzePDRs28PPPP2NmZqaXmgAbNmxgypQprFixgmnTpumtrqgAnhI9AAAgAElEQVQ4Hjd41Wo1x44do0WLFnTp0oXc3FxSUlJo1KgRY8aMwdvbm9atWysdVyjsl19+wc7OjubNmxMWFqb4kfzS0lK6detG+/bt2blzp87qtG7dmvHjx/PJJ5/orIau9ejRg759+/LVV18pHUXv7t+/j6enJxcvXiQ6OppOnTopHUkIIUQ58zTjI6QpLIQQ4m/duHEDOzs7TE1NiYqKom7dukpH0pldu3YxZcoUcnNzMTQ0VDrOM9m+fTuffPIJWVlZesleVFSEtbU1jRs3Zv/+/RgY/O3rDa1Sq9V4e3sze/ZsFi9erNfaomLQaDQcO3aMtWvXsm/fPgoLC9FoNLRo0YLx48czcuRImjRponRMobALFy7Qv39/OnXqREhICLVq1VI6EuvWrWPu3LmkpaXRsGFDndS4fPkyrVu35sKFC7Rt21YnNfThq6++YseOHVy6dEnpKIooLCxkzJgxxMbGEhYWRu/evZWOJIQQohyRmcJCCCFeyLVr1+jbty+WlpbExMRU6oYw/DlP2MHBocI1hOHPo7T5+fkcOnRIL/WqV6+On58f0dHR+Pr66qXmYyqVigMHDrBy5UomT578Us2UFH/vxo0bfPHFF7Rp04aePXuSlZXFhg0b+O233zh//jxeXl5s3ryZpk2bYmNjw7p168jOzlY6tlBAamoqffv2pXv37hw4cKBcNISzs7P5/PPPWbRokc4awgARERE0b96cNm3a6KyGPri7u5OWlkZaWprSURRhZGSEWq1m9OjRODg4sH//fqUjCSGEqGCkKSyEEOIvXblyhT59+vDGG28QGRmp+JFaXSsrKyMmJqbCzRN+zNjYmD59+uj1IrYOHTqwZMkSpk6dyvXr1/VWF8DFxYXY2Fh2797N6NGjKS4u1mt9UX48fPgQX19f7O3tadasGTt27GDMmDGkp6eTmJjI2LFjqVOnDu3bt2fJkiVcvXqV48eP8/bbb/Pll1/SqFEjBgwYwPbt28nPz1f6ryP04NixY9ja2jJgwAACAwMxMjJSOhIAs2fPpkmTJkyZMkWndSIiInB1da3wpyzatGlDy5YtOXDggNJRFFO1alXWrl3L559/ztChQ9myZYvSkYQQQlQgMj5CCCHEf7hw4QL29vZ07dqV4OBgatSooXQknTt16hRvvfUWt27dUvzW+ee1du1ali1bxo0bN/T2w35paSn9+vWjWrVqxMfHU6WKft9vTk1NxdHRkW7duhEUFFQudvsJ3SsrKyMpKQlfX1+Cg4MxNDRkxIgR+Pj40KNHj6f+/C8tLSU5OZmAgAD27t3Lw4cPcXFxQaVS4erqKp9PlVBSUhKDBg3Cy8uLjRs3UrVqVaUjAZCSkkKfPn04ePCgTscA3L9/n9dee42IiAgGDBigszr6MmvWLA4fPszhw4eVjqK4zZs38/7777No0SLmzp1b4Zv+QgghXoyMjxBCCPHMzp07R79+/bC2tiYkJOSlaAgDREVFYWVlVWEbwgAeHh5kZGRw6tQpvdWsWrUqO3bs4MSJE6xbt05vdR/r3LkzKSkp/Pzzzzg6OspOz0ru8uXLfP755zRv3pwBAwZw584dtm3bRlZWFps2baJnz57P1AipWrUqtra2bNq0iczMTEJCQqhduzbvvvsupqamjBo1ivDwcIqKinT4txL6Eh0djbOzM+PGjeP7778vNw3hkpISJk+ezJgxY3Q+FzY2NhYjIyP69u2r0zr64u7uztGjR2UMDPDee++xd+9eFi9ezEcffSSjlYQQQvwjaQoLIYR44scff8TW1hZbW9tydaRWH6Kjo3FyclI6xgtp3Lgxb731FiEhIXqt26JFC1asWMHs2bMVme3YsmVLDh8+zN27d+nXr580ByqZvLw8Nm7cSI8ePWjdujWhoaF89NFHZGRkEBERwfDhw7Xy5lX16tUZOHAgO3fuJCcnh23btvH7778zZMgQGjZsyIQJE/jhhx8oLS3Vwt9K6Nu+fftwc3Nj+vTprFq1qlztoly/fj3p6eksW7ZM57UiIiJwcHCoNM/v1tbWGBsbEx4ernSUcsHT05PY2Fj8/f2ZM2cOctpXCCHE35HxEUIIIQA4fvw4jo6ODBo0iO3bt1fIy9ae17179zAxMSExMbHC755aunQpvr6+er+NXaPR4OzsTF5eHocPH1bk8+fOnTu4uLiQl5dHXFwczZo103sGoR0lJSXExMTg6+tLaGgor776KqNGjcLHxwcrKyu9NvTy8/PZt28farWahIQEzMzMGDFiBCqVirfeeqtcNRfFX9u9ezfe3t5PjtWXJ5mZmbRp04YlS5bw4Ycf6rRWWVkZ5ubmLF26lHHjxum0lj6NGzeO3NxcQkNDlY5Sbly8eBEzMzPq1q37lzviCwsLuX//PiYmJgqkE0IIoQ9PMz5CmsJCCCG4efMm7dq1Y8SIEWzatKncHKnVl8DAQMaPH8/du3epVq2a0nFeSFpaGm3atOHChQu0bdtWr7Vv3bpFhw4dmDlzJp999pleaz/24MEDPDw8SEtLIzY2lnbt2imSQzyfs2fP4uvry65du8jPz2fQoEH4+Pjg5ORULr42s7OzCQoKQq1Wc+TIEVq0aIGXlxcqlYr27dsrHU/8hS1btjBx4kRWrlzJ1KlTlY7zH8aMGcP58+c5efKkzt9MO378ONbW1ty+fZuGDRvqtJY+hYaGMmLECO7cuUPt2rWVjlNuaDSa/3jTqqSkhIiICObMmUPDhg3Jzc0lOTmZevXqKZRSCCGErshMYSGEEE8UFhb+1z9r1KgRO3fuLFczFvUpKiqK/v37l4um04tq3bo1bdu21fsICQALCwvWr1/PwoULOXPmjN7rA9SpU4eIiAjeeustevfuzcmTJxXJIZ5ednY2q1atonPnznTu3JkjR46wYMECMjMz2bt3L4MGDSo3X5tmZmZMmTKFw4cPk56ezoQJE4iIiKBDhw506tSJpUuXcu3aNaVjiv+xZs0aJk6cyMaNG8tlQzg5OZldu3axfv16vZyuiIiI4M0336xUDWGA/v37Y2BgQGxsrNJRypX/2xAuKytj7dq17N+/n6FDhxIfH4+9vT1BQUEKJRRCCKE0aQoLIcRLIDQ0lK+//hrgL+fLVa1aFTc3N6pUefmeFjQaTaWYJ/zvBg8ezL59+xSprVKp8PDwwNvb+2/fiNClGjVqEBQUhJubG3Z2diQmJiqSQ/x3BQUFBAUFMWjQICwsLFi5ciUuLi5cvHiRY8eOMWnSJBo0aKB0zL/VtGlTZs2axZkzZ7hw4QKenp5s376d119/HWtra9asWUNmZqbSMV9aX375JTNmzMDPz4/33ntP6Tj/obi4mMmTJzN27Fh69uypl5rh4eG4urrqpZY+1apVC0dHRw4cOKB0lHJt165dXL9+HXd3dxYtWoSBgQG1a9cmJydH6WhCCCEU8vL99C+EEC+Z7du3o1Kp+OKLLzh79ux/nX/5ss7FPHv2LFlZWZWqKezp6cnp06e5ceOG3msbGBiwYcMGcnNzmT9/vt7rP2ZoaMjWrVuZMGECzs7O7N+/X7Es4k8ajYZjx47xwQcf0LBhQ8aOHUu9evWIjo4mPT2dL7/8kjZt2igd87m0bduWf/3rX6SlpXHq1Cl69erFihUrsLS0xN7eni1btpCXl6d0zJeCRqNh7ty5LFy4kKCgIEaNGqV0pL+0du1aMjIy+Oqrr/RS79atW5w5c4aBAwfqpZ6+ubu7Ex4eTklJidJRyqVbt25x6tQprK2tcXd3B+DEiRM8ePCAkSNHKpxOCCGEUqQpLIQQldjatWtZuHAh586dY8OGDVy/fh2A0tJShZOVH9HR0bRr144mTZooHUVrunbtSpMmTRTbLWxsbMzmzZtZsWIFKSkpimQAqFKlCitWrGD+/PkMHTqUHTt2KJblZXbjxo0nDd8ePXpw6dIlVq1aRVZWFv7+/vTv37/SjK0xMDDgzTffZMWKFVy/fp2kpCRat27NnDlzMDMzw83NDbVazaNHj5SOWilpNBo+/vhjVq1aRWhoKJ6enkpH+ku3b99mwYIFfPHFF5iamuqlZmRkJGZmZrz55pt6qadvrq6u5OXlceTIEaWjlEv79+/n+vXreHp6YmBgQHp6OocPH6ZFixbUrVtX6XhCCCEUIk1hIYSopAIDA5k3bx7R0dG0aNGC7OxsNm7cCFBpGjDaEBUVhbOzs9IxtMrAwABPT0/FmsIAgwYNYuzYsfj4+PDw4UPFchgYGPDZZ5+xdu1axo0bx6pVqxTL8jJ5+PAhfn5+2Nvb06xZM7Zv387o0aO5du0aP/zwA2PHjqVOnTpKx9SpKlWq0Lt3b7777jtu375NaGgo9evXZ+LEiZiamqJSqQgNDVVszEplU1payoQJE9i2bVu5Hwk0c+ZMWrVqxcSJE/VWMzw8nIEDB1baMVHGxsbY2NjICIn/4tKlS3Tv3h0jIyMyMjKIjIzk7t279OzZs9yP6hFCCKE7Bn81W/I/PsjAQPM0HyeEEKL80Gg05OfnU79+/Se/VqlUTJs2je7duyucrnz47bffMDY2fnLRXGVy8OBB7OzsyMrKwsTERJEM9+/fp1OnTjg7O7NhwwZFMvw7tVqNt7c3s2fPZvHixS/tyBRdKSsr4+DBg/j6+rJ3714MDQ0ZMWIEPj4+9OjRQ/69/8cff/xBZGQkarWa8PBwatasyeDBg1GpVNja2sqbds+hpKQEHx8fIiMjiYqKwtraWulI/9UPP/yAvb09R48e1dtzcUFBAa+99hr+/v4MHjxYLzWV8M033/Ddd99x9epV+X7zf6xZs4b4+HgcHR2JjIyka9eu2NnZYWdnp3Q0IYQQOmJgYIBGo/nbJ0RpCgshRCXj6+uLoaEhDRs2fPJiv7S0lOLiYqZNm0bHjh2ZNGkSGo3mpf+hKSQkhDFjxnDv3j2MjIyUjqNVpaWlmJub89VXXzFu3DjFcjxugERFReHo6KhYjsciIyMZMmQI7777LmvXrq20u+b06cqVK/j6+uLv709GRgaOjo74+Pjg5uZGzZo1lY5Xrt2/f5/9+/ejVquJi4vD2NiY4cOHo1KpsLa2fum/Rz+NwsJCVCoVhw4dIjY2li5duigd6b8qKiqic+fO2NjYsHnzZr3VjY6Oxs3Njbt371bqHfpXr16lZcuW/PTTT3To0EHpOOVKSUkJs2bNorCwkBYtWjBmzJgnbxj/1evBX3/9lfz8fB4+fEjv3r3le5EQQlRA0hQWQoiXzOjRozl//jwdOnSguLgYd3d3Ro4c+eQFf3JyMu+88w7BwcF07txZ6biKmzBhApmZmYSFhSkdRSfGjx9PVlYW4eHhiuaYNm0agYGBnD9//snOdSWlpKTg6uqKi4sLvr6+VKtWTelIFU5+fj579uzB19eXo0eP0qFDB3x8fBg1ahQNGzZUOl6FlJuby969ewkICCA5OZlmzZrh5eWFSqWiY8eO0pT5C7///jtDhgzh7NmzxMfH065dO6Uj/a3ly5ezdOlSLl++jLGxsd7qTpkyhcuXLxMbG6u3mkpp3749I0eO5LPPPlM6Srl148YNTE1NqVGjxl82hPPy8vjmm2+4fPkyRkZGFBUVsX37dmrVqqVQYiGEEM/jaZrCsj1GCCEqiWvXrnHv3j1SU1PZunUrnTp14vz58wBPXvD36dOHcePGsX//fkXnvJYHGo2mUs4T/neenp7ExcVx//59RXN8+eWXvPrqq3z44YeK5nisV69eJCUlkZCQgIeHB7///rvSkSqEkpISIiIiGDFiBObm5sybN4+3336bH3/8kXPnzjFz5kxpCL8AExMTPvjgAw4ePMjNmzeZMmUKcXFxWFlZ0aFDB5YsWcLVq1eVjlluPHjwABcXFy5evMihQ4fKfUM4IyODf/3rXyxdulSvDWGNRvNknvDLwMPDQ+YK/4Pw8HC+++47SktL//LNpvr16zNz5kz8/f357rvvKC4u5tKlSwokFUIIoWvSFBZCiEqgpKQEAwMDbt68yc2bNzEyMqJjx45ERUXh5+fHunXrnnxs06ZNOX78OA8ePFAwsfIuXLhARkZGub6M6EXZ29tjZGREVFSUojlq1qyJn58fAQEB7N27V9Esj3Xu3JmUlBR+/vlnHB0dyc/PVzpSuXXu3DlmzJiBpaUlnp6elJSUEBgYyK1bt1i9ejVdunSRXaxaZmlpyYwZMzh16hRpaWmMGDGCXbt20bJlS95++21WrlzJrVu3lI6pmLy8PBwcHLh9+zbJycm0aNFC6Uj/aMaMGbRr147x48frte6FCxe4fv06rq6ueq2rFHd3d06ePMnt27eVjlJuTZo0ibFjxz6ZX15WVvYfH1OlShWMjIwoKCggNzeXvLw8CgsLuXz5sr7jCiGE0CFpCgshRAXn7e1NamoqzZo1Y8KECQwcOJB169bx1Vdf4erqSp06ddi5cyfLli0DYNSoUSxZsuSl39EXFRVFq1ateP3115WOojM1atTAxcWFffv2KR2Ft956i7lz5/L++++TlZWldBwAWrZsSUpKCnfv3sXW1pbs7GylI5UbOTk5Txq+VlZWpKSkMH/+fDIzMwkODsbNzU3GbuhJq1atmD9/PhcuXODMmTPY2tqyZs0aGjduTL9+/di0aRN3795VOqbe5ObmYmdnx6NHj0hOTqZJkyZKR/pH8fHxBAUFsX79er1fJBgeHk6bNm0qRONcG7p160bDhg0JDQ1VOkq51qBBA+DPneSPZ+vHxMRQWFhIfn4+1tbW9OzZkw8//JBXXnkFe3t7bt68iYeHB/7+/kpGF0IIoUUyU1gIISqo0tJS7OzsqF27NpGRkU9+//vvv+fUqVOYmpqyZMkSAL7++msuXrzI1q1b5Wb7/9G/f3/at2/PmjVrlI6iU3v27GH8+PHk5uZSo0YNRbMUFRVhbW2NpaUlBw4cKDe7S+/cuYOLiwt5eXnEx8fTtGlTpSMporCwkLCwMHx9fYmKisLc3JwxY8bg7e1N27ZtlY4n/k1ZWRlHjx5FrVYTFBTEvXv3GDBgACqVCnd390p7mdjt27fp378/NWvWJDY2ltdee03pSP+osLAQKysr+vXrx8aNG/Vev3fv3nTv3p0VK1bovbZS3n//fa5fv674KZmKZNu2bURHRxMYGAj8+brx6tWrfPzxx7z++utPLg09fPgwXl5efPjhh8yaNUvJyEIIIf6BzBQWQohKqrS0FE9PT954440nDeH09HTKysqYMGECjo6O/PjjjxQXFwPwyy+/kJ+f/5dHBF9GDx8+5NChQ5V6nvBjLi4uFBUVkZCQoHQUqlevjr+/P7GxsezYsUPpOE8YGxuTkJBAkyZNsLGx4cKFC0pH0huNRsPx48eZNGkSDRs2xNvbm3r16hEdHc3169dZunSpNITLoSpVqmBjY8O6deu4desWkZGRmJmZMWXKFExNTRk+fDghISEUFBQoHVVr0tPT6d27N/Xr1ycxMbFCNIQBVq1axZ07d/jiiy/0XvvevXscOXLkpRkd8Zi7uzsJCQmKz9OvSJo3b84ff/zx5NfFxcUUFRXRvn17atasSVFRERqNBhsbG65cucI333zDv/71LwUTCyGE0AZpCgshRAVUtWpVqlSpQr169QD44IMPGDVqFP369WP79u00a9aMN954g759+/LRRx+xc+dOpk2bJse9/8cPP/xAlSpV6Nu3r9JRdK5OnTo4ODgQEhKidBTgz5vhlyxZwtSpU0lPT1c6zhN16tQhIiKCt956iz59+nDy5EmlI+nUzZs3+fLLL2nbti3W1tZcuHCBb775huzsbPz9/enfv7+cKqggDA0NcXBwYNu2bWRlZaFWqzEwMGD06NGYmZkxduxYYmJiKCkpUTrqc7t8+TJ9+vShWbNmxMTEULduXaUjPZUbN26wePFivvrqK0Wa2NHR0dSpUwcbGxu911aSnZ0dRkZGREdHKx2lwujatSs3b95kyJAhfPrppyxbtgwfH58nf169enUMDAw4ceIEJ0+exMTEhJ9//llmDAshRAUn4yOEEKICuXHjBklJSXh7e1NUVMTAgQPJysri1VdfJSQkBD8/P65evUqrVq1wcHDA39+fwsJCvL296datm9Lxy41JkyZx7dq1l+Zo6datW/n000/JzMzE0NBQ6TiUlpZia2tL1apVSUhIeDLPsDwoKSnhvffeY+/evYSGhmJra6t0JK159OgRISEh+Pr6kpiYyOuvv463tzdjxoyhefPmSscTWvbgwQNCQ0NRq9XExMRQv359hg0bhkqlomfPnuXq6+7vnD9/nv79+9OtWzeCgoKeHGOvCIYOHUpGRgZHjhxR5N975MiRlJaWsmfPHr3XVtrw4cOpVq0au3btUjpKhVFWVsby5cuxtLSkYcOG2NnZPfmzixcvEh4eTkhICL169aJv3744OTmVi9cUQggh/trTjI+QprAQQlQQly9fxs7ODh8fnyfHUGNiYli8eDFffvklffr0AWDx4sXExcWRlJRElSpVKCkpkRft/0aj0fD666/z8ccfM3XqVKXj6EVubi7m5uYkJiaWm93Rv/76K506deKLL74od/8PZWVlzJw5k++++46AgAA8PDyUjvTcysrKOHjwIH5+fuzdu5cqVaowYsQIfHx86NmzZ7mZ6yx06+7duwQHB6NWqzl48CCWlpZ4eXmhUqno3Llzuf08OH36NAMGDMDOzo5du3ZRvXp1pSM9tZiYGFxcXDh58iRdu3bVe/2SkhJMTU1Zs2YNY8aM0Xt9pe3atYspU6aQk5Mjp6Re0IwZM8jNzaV69epMnz6ddu3a/cfHPO4VlNfvJUII8TKSprAQQlQSP//8M3369GHFihW88847T37/8cVQffv2xcTEBPiz2ebj40NISMiT3xP/X1paGm3atCEtLY1WrVopHUdv+vXrh5WVVbm6WO/7779n6tSpnDlzhjZt2igd53/RaDR8+eWXLFiwgC1btjB27FilIz2TK1eu4Ofnh7+/Pzdv3mTAgAH4+Pjg7u5eoXZaCu27desWgYGBBAQEcOLECVq3bo1KpUKlUpWr74mHDx/GxcUFDw8Ptm7dWqHe3CwsLKRjx444ODiwfv16RTIcOnSIvn37kpOTg7GxsSIZlJSXl4eJiQkxMTHY29srHadC2rt3L5GRkZw/fx61Wk2DBg2oX7/+//qY1NRU8vLyCAwMpKioiGXLllWYed9CCFHZyUVzQghRSdy6dQsDA4MnDeHJkyczadIkQkNDcXJyokaNGpw9e5YrV66wevVqatWqVWFmLupbdHQ0zZs3p2XLlkpH0avBgwezf/9+ytObvO+99x62trZ4e3uXu3mnBgYGfPbZZ3z77beMGzeO1atX6z1DZmYmjx49euqPz8/P5/vvv8fGxoZWrVqxf/9+pkyZQkZGBlFRUXh5eUlDWGBhYcG0adM4fvw4V69eZcyYMQQFBdG6dWu6du3K8uXLuXnzpqIZExISGDBgACNHjmT79u0VqiEMsGLFCvLy8liyZIliGcLDw7G2tn4pG8IA9evXp2/fvhw4cEDpKBXWa6+9Rv369YmNjaVFixbUr1+fe/fucezYMSZPnsz48eMZN24cmzZtIigoCBMTE2kICyFEBSM7hYUQooL4+uuvyc/PJyYmhqZNm9KnTx+2bduGl5cXDg4ObN68mWvXrvHbb78RGxv7H7s5xJ+cnJxo0aKFYru3lHLjxg2aNm3KqVOnePPNN5WO8//Yu/OAGvP2j+Pv0x4pSUT2fSwjamyV7FFIZc2adSr7Pvbd2LexzaCyhJCiaSNbQpYYS5pGUUYbWdPeOb8/+nWep2cwzOAuvq9/PI/uc87nNp3tuq/v9VVKTEykcePGTJo0iblz50od543279/PkCFDmDlzJosWLfqky2NTU1PZsWMHe/bsISEhgZiYGIyNjd96fF5eHiEhIXh6euLn50eZMmVwcnJi6NChNGvWTCzlFd7brVu32L9/PwcOHOD+/ftYWFgwYMAA+vTp896rTl6/fo2vry/e3t707t37H40t+PXXX3F0dMTNzY3Vq1eXuN/hBw8e0LBhQzZv3lxkZc/n1rhxY5ycnJg1a5ZkGaS2ceNGVq9eTXx8fIn7PSoucnJy0NDQIC8vD1VVVRwdHXn06BGDBw+mY8eOaGpqsn37drKzs5UXT1NSUgCoWLGilNEFQRC+emJ8hCAIQgl26dIl4uLiSE9Px9nZmZiYGKZMmUKZMmU4dOgQAFevXsXa2prAwECaNWtGRkYGOjo6qKqqSpy+eMrMzKRcuXJ4e3vTo0cPqeN8dmZmZlhbWytnUhcXhUXXiIgISWZvvo+AgAAcHR0ZPnw4mzZt+uibRmVnZ9OlSxfu3buHhYUFrq6uzJgxA09PT+rXr/+X41NSUli1ahX79u0jLS2NHj16MHToULp27Vqi5q4KxY9CoSAiIoIDBw5w8OBBHj9+TKdOnRgwYAD29vbo6uq+8XZZWVlMmTKFixcvMnz4cE6dOsXy5cvf+Pv7LnFxccTHx9OuXbsSWcizt7cnNTWVsLAwyTbzu3//PrVq1eK3337j22+/lSRDcRAfH0+NGjWIjIykWbNmUsf5Ily6dAknJyfGjRvHpEmTWL58OREREXh7e6OhocGWLVvYsmULcrkcOzs7li9fLnVkQRCEr5YoCguCIJRQly9fZurUqZiYmHD37l3Kli3LoUOH2Lx5Mzo6OgwePJicnBy0tLRwdHSkY8eOuLq6Sh272AsMDKRXr16kpaWho6MjdZzPbtmyZezdu5eoqCipoxShUCjo378/d+7c4erVq2hpaUkd6Y3CwsLo3r073bt3x8PD419vXhQQEEBUVBT9+vWjatWqhIWF0aZNG1RVVUlMTFRuIvmmrv8///wTBwcHhg4dSv/+/cWSXeGTyM/P5+zZs+zfv58jR46QkZHBoUOHsLGx+cvFRx8fHw4fPoybmxvm5uZMnz4dAzVlElQAACAASURBVAMDZsyYgUKhKJEF3g8VEBBAjx49uHbtGiYmJpLl+Omnn1ixYgUJCQlfxb/7u5iYmNCrVy8WLFggdZQSLz8/H1VVVWJiYujRowfVqlWjVKlS7N27lzJlypCTk0NsbCwA6urqtGzZks2bN9O/f3+JkwuCIHydxExhQRCEEuqPP/7A3NycjRs34u7uTn5+PllZWbi5uTF06FDS0tLQ0tJCLpeTlJT01c4M/FBBQUFYWlp+lQVhKOhgu3v3LtHR0VJHKUImk7FlyxbS0tKYN2+e1HHeytLSkjNnznDy5EkcHR3Jysr6R/eTk5PDoEGDmD59OpcvX2bKlCn4+vpiaWmpLLSlpaVx+vTpt46BMTY25vLly7i5uYmCsPDJqKqq0qFDB3755ReSk5M5dOgQVlZWfykIy+Vyzpw5Q6tWrTA3NwegXLly1KlTB+AvhcnCZhO5XP4ZzuLzyMrKYvz48bi5uUlaEIaCERzdu3f/6gvCAL169RJzhT8SVVVV8vPzqVevHj/88ANXrlwhLy+PMmXKkJ2djYaGBvXr1+ebb77B2NgYU1NTsXJNEAShmBNFYUEQhGIoIyNDuSGUjo4OYWFhrF69mpkzZwKwbds27O3tad68OQYGBvTt21fKuCVGYGAg3bp1kzqGZL755hvq16/P0aNHpY7yFwYGBuzYsYM1a9YQFhYmdZy3atasGdHR0Xh6eqKhofFeG/c9evSImTNnMm3aNLKzs7l9+zYvXrzg9u3bHDx4kG+//ZaLFy/+5XaWlpY8fvz4jfcpij3C56ahoUGPHj3eOD7i0aNHKBQKmjZtChSMN5HL5eTn57/xvmQyGZGRkSxbtgw7OzvmzZvHy5cvP2n+T23lypW8evWKRYsWSZojPT2dU6dO0b17d0lzFBd2dnbcuHGD+Ph4qaN8EVRVVYmMjGTjxo34+voybNgwADQ1NQFQUVHh4cOH7Ny5E4VCgbGxMfn5+e98PRAEQRCkI4rCgiAIxcTZs2eJiooiOTkZOzs76tWrh1wu58qVK1SvXp3KlStz4sQJhg4dyg8//MCECRP48ccfOX78uNTRS4TY2Fj++OMPunbtKnUUSTk4OBTLojCAra0tzs7ODBs2jPT0dKnjvJW+vj76+vqoqKi8szibkpJC3759admyJTExMdjZ2aGpqUmNGjVISkri1atXyGQyHjx4QKVKlQCUX5pv3LhBbm4uhoaGX1Q3pfBlSk1NJS8vj6pVqwIFM20zMjKUv9f/e/Hk6dOneHh4kJ2dzdy5c7l79y4REREAeHp6cvLkyc97Av9SXFwcy5cvZ9WqVZQtW1bSLKGhoaioqNC+fXtJcxQXJiYmVKtWjWPHjkkd5YvRvHlztm3bRrt27ejSpQtJSUkcPnyYTZs2MXz4cNq0acPly5cZNWoUzZs3Z9SoUYwbN45WrVqRmJgodXxBEAThv4iZwoIgCMWAm5sbZ86coXr16pQtW5a5c+fyzTff/OW4pKQknJyc8PHxeeuycuHNNm/ezIoVK776XcivXLlCixYtSEhIUBZwipOXL1/y7bff0rVrV7Zt2yZ1nA/m4+NDcHAw27dvJysri5o1axITE0OZMmWKHNejRw8qVKhAq1at2LZtG3379mXQoEEYGxsDcP36dQYNGsSdO3ekOA1B+CC3b99m0KBB3LhxA4CNGzfy7Nkzxo0bR7ly5f5yfGZmJo8ePVKOl5g/fz4xMTHs378fDw8PNmzYQFpaGpaWlri6uipHUhRXPXv25NmzZ5w7d07y95fRo0eTmJiIv7+/pDmKk3HjxnH37t0Sd7GhOFMoFGRmZmJiYkJmZiatW7dGTU2N9u3bU6VKFeWqrP79+1O7dm0WLlzIkiVL8Pf35+TJk5JfPBEEQfgaiJnCgiAIJUBERATXrl3jzp07rF27lgoVKig7pqBg5mJhp2BWVhb5+fnk5uZKFbfECgoKomvXrpJ/YZeamZkZVapUwdfXV+oob6Srq4unpyc///wzgYGBUsd5bxMmTKBq1ar8+OOPVK9eXbkRZKtWrQgJCeHMmTPMmjWLc+fOAQVFM7lcTlBQEPPmzUNPT49+/fop5xSrqanRpEkT4uLipDwtQXgvampqGBgYcPbsWXbs2IGPjw/m5uZvLAgDaGtrU6dOHV6/fs3x48e5ffs2FhYWAAwbNozr169z584d1NXVsba2Zvfu3Z/zdD7I8ePHCQgIYPPmzZK/vygUCvz9/cXoiP9hZ2fHmTNnePbsmdRRvhgymYxSpUrh6upKRkYG48aNw8vLi1GjRikLwsnJycjlclxdXVFTU8PNzY1atWopVw6IpjNBEATpqUkdQBAE4WuXk5ODoaEhAA0aNKB8+fIcP36cunXroqWlhampKc7OzmhqahIUFISzszMVKlSQOHXJkpWVxalTp9i7d6/UUSQnk8mwt7fn6NGjjBs3Tuo4b2RlZcXEiRMZMWIEt2/ffmthSUppaWno6emhplbwUer69etMnTqVCRMmFDmuZcuWDBkyhG+++YYWLVrg7OzMhg0bsLKyIjo6mpMnT1K6dGmysrLYs2cPAQEBODg4cP/+fV69ekVOTo4UpycIH6RBgwb07NmTMWPG0KBBA5ydnenUqdM7b5Oeno6ZmRnJycl4enpibW0NFLxea2lpIZPJqFixIlOnTsXR0VF5u7y8PG7evEm5cuWoUaPGpzytv5WZmcn48eMZN24c3377raRZoOB1KCkpCRsbG6mjFCtWVlbo6OgQEBDAwIEDpY7zRZk4cSLGxsb07duX5cuXM2zYMBQKBQ4ODpQuXZoTJ05w5swZmjdvTkBAAH/88YfyfVMul4uN6ARBECQmOoUFQRAkZmlpSenSpXFyciIiIoJLly5RoUIFTp8+zaBBg/jjjz8YMmQINjY2bNiwgfnz50sducQ5f/48OTk5dOzYUeooxYK9vT1nz57lyZMnUkd5q6VLl1K2bFnGjh0rdZQiUlNTGTt2LI6OjsycOZOAgACgYPm4np4eT58+ZfPmzURHRwNQo0YNxowZw9WrV9myZQsuLi4cO3aMsLAwTExM2LNnDwBhYWHI5XLl+Ahzc3NcXFyoV6+eNCcqCB9owoQJnD59ml27djF06FDGjh3Lr7/+qlzpUvjnzZs3SU1NRUdHh3PnztG1a1dq1qyJlpYWAFpaWuTl5TFgwABKly7N5MmTKV26tPJx5s2bh4ODQ5FRAM+fP/+MZ/ofP/74I1lZWSxYsECSx/9fv/76K99++y3VqlWTOkqxoq6ujq2tLX5+flJH+SL16dOHQ4cO4e3tDYCrqytxcXHs3buXc+fOsXr1akaPHs2FCxfYtWsXL1++ZPbs2Tg5ObFixQqJ0wuCIHzdRFFYEARBAosXL1YWjQA8PDxQKBTMnz+fpk2bsnXrVubMmUObNm1wd3enffv29OzZEzs7OwlTl1yBgYGYm5ujq6srdZRiwdLSEn19/WK9SaG2tja7d+/G29ubQ4cOSZrl5cuXyv999uxZsrOzcXd3R1dXl9mzZyt/tmrVKho1asSvv/6q3NDP1taWhQsXKpfJtm3blsTERMqUKUPjxo1ZsmQJ/fr1o1+/ftSsWRNTU1MADAwM6N69Oyoq4qOaUHJUqlRJ2dk/c+ZM6tevr/wdLvxz4cKFBAUFAVChQgUePXpEaGio8j6ePHnCmjVrUFNTY968ecrX7SdPnuDs7MzFixepVKmSsnt40aJF9O7dGz09PebOnUtmZuZnOdd79+6xYsUK1qxZg56e3md5zL8jRke8nZ2dHYGBgWRnZ0sd5YtkYWGh3MQ2ISGByZMnA9CkSROmTZvGggUL2L9/P82aNePRo0fUqlULGxsbVq1aVeR9VBAEQfi8xDcNQRCEz0yhUBASEsLs2bNJSEgACjqj9u/fT+vWrdHU1FQe++rVK+WMUeGfK5wnLBRQU1OjZ8+e+Pj4SB3lnczMzJgzZw4uLi4kJyd/1sdOTk6mX79+1K1bl/Hjx3Px4kUALl68SK9evahZsyb9+vWjcuXKvHz5kgoVKtC6dWt+++03AgIC+P7779mxYwePHj0iOTlZOcuyVKlSREREUKtWLdzc3Fi/fj316tXD398fLy8v5bJaQSjpqlSpotxI7r8NGzaMffv2ce3aNby9vYmNjaVfv37Kny9dupQnT54wZ84c5d/5+voyfvx4SpUqxahRo6hfvz76+vrcu3ePq1evsnLlSo4dO8aRI0e4e/eu8nYpKSmf5NwUCgVHjhyhVatWDBgw4JM8xodKSUnh8uXL2NraSh2lWOratSvZ2dmcPn1a6ihfLE1NTRQKBZUqVeLnn3/mjz/+IDY2lnnz5vHgwQM0NDQAaNGiBSNGjGDo0KFs27aNmJgY8VlXEARBIrL3GfAuk8kUYhC8IAjCx5GVlYWtrS3Vq1cnJSWFnTt3YmRkBMClS5f48ccfqV27NsbGxsyZMwdfX1+6dOkiceqSKyEhgerVq3Pjxg2aNm0qdZxi4/jx4/Tp04fHjx9TpkwZqeO8VW5uLq1bt6ZSpUocO3bss23ktG3bNuLi4pg0aRJbt27l7t27bN68mZiYGBo2bEi5cuVwc3MjPT0dT09PUlNTefnyJbVr1y7c6Zdq1apx7NgxAgICiI6OxsTEBF9fXzp37szs2bPFLEXhq5Sens6yZcs4evQoFhYWODg4KDemio6OpkOHDly+fBljY2NkMhnbtm0jIiKCUaNG0bJlSyZOnEjTpk0ZOXIkr1+/Ji8vDz09PZ4/f87IkSNp27YtY8aMYe/evWzevJmsrCzGjh2Li4uL8rn5b19HFAoFcrmcrKysIqMtpOTh4cHUqVNJSUkRry1vUTiqZOvWrVJH+eJ16tSJhIQEXr58Sbdu3XB3d3/jcaampjRs2FA5SkkQBEH4eP7/c887P/SITmFBEITPLDc3lwoVKjBq1Cjq1auHq6ursouwdu3aDB48mMjISE6cOIGPj48oCP9LQUFBVKpUqVhsAlScdO7cGTU1NeUy7uJKXV2d3bt3c+LECXbt2vVJHiMrKws3NzfS0tKUf3f8+HFatWpFpUqV6N69O+rq6iQlJWFhYUG5cuWIiYkhJCSEtLQ0evfuTfny5dHQ0CA3NxeAjIwMFAoFGRkZTJw4kQ4dOhAaGkqfPn2YNWuWKNoIXy0dHR2WLVvG3bt32bhxo7IgfOPGDeU4lSpVqiCTyfjzzz+ZPXs20dHRyGQynj17xh9//IGzszMApUuXRkdHBygYEySTyTAzM+PgwYOcP3+esLAwVq9eTUhIiHJljkwm+9cziGUyGaqqqsWmIAwFoyNsbGzEa8s72NnZ4efnp5xvLXw6J0+exNPTk9OnTxcpCN++fZvTp0/j4uLCd999R9WqVVmxYgUvXrxg9erVrFu3TvlcFQRBED490SksCILwiSUnJ3PkyBEMDAywtLTE2NiY5ORkjIyMuH//PmvWrOHx48e4u7tTqlQpoGB39aysLOWXXeGfs7e3R19f/5MVFEuyvn37oqqqyv79+6WO8rfWrFnDggULuHXrFjVq1Pgo91nYMXjw4EFcXFzw8PCgZ8+eAOzatYvDhw8TEBDA2bNnmTx5MteuXfvLfeTm5tK0aVOWLl3Kq1evOHHiBIMGDcLLywsVFRW2b9+uXDIrCMLbpaWlcfLkSUxNTalTpw55eXmoqanx4MED9u7dy4EDB3j8+DGlSpXi3LlzVK5cWVkAvXDhAr1792bZsmUMGzaMgQMH0qNHD/r37w9Aly5dGD58ODY2Nvzyyy/s2bOH1NRUlixZwrBhw946u1sul5eIud45OTmUL1+eHTt20LdvX6njFFuPHj2iSpUqRERE0KJFC6njfBXkcjnbt2/H09OTSpUqER4eTpMmTfj222/p1KkTtra2uLu74+/vT2xsLK1bt8bPz4/IyEjlKjpBEAThnxGdwoIgCBJLS0vDwsKC2NhY7t69y4ABA7hx4wZly5YFoHr16kyYMAE9PT1cXV3Jy8sDCma+ioLwv5eTk0NoaKiYJ/wWDg4O/PrrryVi452JEyfSrFkz9u3bx4deqC48PiIiggEDBrB3714eP36MTCYjKyuL8+fP06VLF/bt26e8zbBhw5DL5ZiamjJ9+nTq1avH/PnziYiIACAxMREo6GS2srIiJiaGnj17YmFhwcyZM1FVVWXOnDmiICx8df5pI4mBgQH9+vVTziFWU1NDoVBQo0YN5syZw+3bt2nbti3169cnPDwcVVVVHj16xJw5c3B1dWX27NnK522nTp04duwY2dnZ7N+/n8ePH2NlZcXFixd59OgRW7ZsYdWqVfzyyy/8/vvvb81UEgrCAGFhYWRkZIiVRX/D2NgYMzMz/Pz8pI7y1VBRUaFChQpcvnyZTp06kZqaSmhoKOvWrcPW1pZdu3YRHh5Ohw4duHLlClu3bqVXr16kp6dLHV0QBOGrUDI+6QiCIJRQsbGxGBoasnbtWhYuXMjQoUNZvXo14eHhZGVloaKiQt26dZk8eTIvXrxg1qxZUkf+oly4cIHXr1/TuXNnqaMUSzY2NmRnZ3Pq1Cmpo/wtVVVVgoODmTVr1gfPA5XJZDx8+JApU6ZQu3Ztnj17xpAhQ4CCwpOmpiaTJk0iNTWV2NhYoOCL7KhRozA3NyciIgIvLy80NDT46aefeP36Nc7Ozhw4cIC9e/cSExND//79KVu2LGPGjOH69evs2rWL2rVrf/R/B0EozhITE/Hy8iI9Pf0fF4f/W+EM4Pz8fOLj44mLiyMoKIj+/ftz9OhRRo8ezZMnT9i8eTNubm4AbNmyhQsXLhAbG4u5uTlbtmxh2bJlVKpUCTMzM9auXUubNm0YOHAgkZGRvHr1CuAvIwWGDh1KWlpaiRg14O/vj6WlpfKCs/B2vXr1EkXhz8zR0RFfX19WrVpVZNXW6dOnuX79Ou3atcPZ2Rl1dXUiIiK4ceMGenp6EiYWBEH4eoiisCAIwidUuXJl8vPzCQ0NBWDEiBFYW1uzZ88eoqKilMfVr1+fFStWiKLwRxYUFESrVq3Q19eXOkqxpKurS8eOHfHx8ZE6ynvR1tb+oIJwXl4ejx8/BuC3337j1atXLFmyhHHjxqGiokJAQABqamqsXr2axo0bU6dOHY4ePaq8fUpKSpFuJS0tLdLS0ihdujQjRozg8OHDbNu2jTFjxlC9evWPd6KCUAJFR0fTokULdu/ejYqKykfbFLJwfm/16tWV3fyRkZE4Ojpy9+5dli5dirm5OVDQpbx69WpGjRpFREQER44cwczMDEtLS6CgG7mQj48PtWvXpn79+uTn5xfpCk5LS+Phw4ckJSWhoqLCxYsXGTduHHv27OH69esf5bw+Jn9/f7p37y51jBLBzs6OO3fuKC8ACp9Hz5498fLyYtmyZVy6dAmAW7duoaWlRdeuXSlVqhRJSUmcP38eZ2dnDA0NJU4sCILwdRBFYUEQhI8sIiKCgwcP8uuvv1KlShUaNmzI6dOnlcWlwYMH07BhQ9zc3JSdVDKZjHr16okun48sMDBQuYmR8GYODg74+fmRn58vdZR/5dWrV0RHRxMVFUVmZiZQ0AVsYGDAn3/+qRzlUlgkHjBgAKGhoaSkpCiPbdu2LadPn1bep7GxMVevXsXX15eQkBB27NiBvb09UDCPef/+/Zw/f/6DZ3jm5eWRn59fIjoQBeF93Lx5k7Zt22JqasqxY8eU8/E/tgYNGqBQKGjevDn+/v60a9eOJk2aYGpqyrlz54iLiyMxMVHZpV9YSE5KSipyPytWrGDjxo0sX74cXV3dv2zOZmBgQMeOHVm+fDmzZs1i1apV/Pbbbxw+fBhPT0/l5rDFQUxMDPfu3cPW1lbqKCVCo0aNqFWrlugWlkCbNm24evUqtWvX5uXLl6xatYr27dtTvnx5nj17xsmTJ3nx4gXNmjXj+fPneHh4MHv2bOzt7Vm5cqXU8QVBEL5IoigsCILwEfn6+tKmTRv27dvHyJEjWbduHStWrCA0NJTdu3fz4sULAKZPn45cLicsLEzixF+uxMREbt68KeYJ/42ePXuSlpZGeHi41FH+kdzcXPbu3cuUKVMwNzdn7NixWFtbK38uk8kwMDCgadOmxMTEKLsXbWxsSE1N5cGDBwBoampiampKbm6ucm5w165dcXBwYPny5QwaNAgbGxscHByU962urv6PMufn5zN16lQaN25MfHz8PzxzQSgeLl++TLt27ejQoQOHDx9GU1Pzkz7efz+Hd+3aRWJiIgsWLEBNTY2aNWvStWtXfvjhB0JDQxk/fjx6enrUrVsXKOj+HzduHB4eHixZsgQ7O7u3XpwZMWIEUVFR+Pn54eLiwrlz59i+fTs3btxgypQpH2U8xsfg7++v7HgW/p5MJsPOzk4UhSVStmxZDA0N0dXVpW3btujp6fHgwQN27NhBVFQUzZs3x8zMDB0dHVq1asWFCxcICwujT58+UkcXBEH4IomisCAIwkeSn5/P9u3b2b59O8eOHePQoUN4e3ujqanJ+vXrOXz4ML/88gv79u3jxo0baGtrU6ZMGaljf7GCgoIwNDSkefPmUkcp1ipUqIC5uXmRsQklRWpqKnPnzuXatWuYmJgQHh7OqVOnUCgUypEtMpkMDQ0NmjZtysuXL4mJiUEul1OuXDm0tLS4du2a8v6qVKlC48aN8fLyAgoKxfPnz8fHx4fHjx+zZs2aIsvP/ylNTU0WLVqEkZERFhYW3L1791/fpyBI4dy5c3Tq1IlevXqxb9++f3yh5N/q0aMHbdq0QUVFhR9++IH4+Hj69u1LSkoKW7ZsAWDr1q24ubmRnJxMcHAwFhYWKBSKIl3C3t7eBAQEAGBkZMT48eP5888/lZ3BRkZGbN++ncTERDIyMj7/ib5B4eiIjzWu42tgZ2fH+fPnefLkidRRvmrlypVj9uzZtGvXjtjYWNq1a0evXr2AgtU79+/f59y5c/j7+1OzZk3l7YrLc08QBOFLIIrCgiAIH0l+fj7a2trKjqQ6deogk8m4ceMGLVu2ZO3atWhqavLjjz+yePFiatSoQbNmzSRO/eUKCgrC2tq6xOweLyUHBweOHj1abDrf3tfOnTtRU1OjX79+uLq60qBBA549e0aZMmWoUaOG8jhVVVVkMhnTpk3D39+fR48eAQVLy0+cOKE8rkyZMlhZWRVZ+i6TyTA2Nv7o2cuUKUNAQACmpqZYWlpy9erVj/4YgvAphYSE0LVrV4YOHcqOHTv+MoJBKq1btyY4OJjU1FR27txJ586d2bhxI25ubuTl5bFr1y6qVav2l9ulp6czdOhQpk6dqrxQ06VLF0xMTHB3dyc1NZVnz56xbNky9PX1KV269Oc+tb948eIFYWFhYnTEBzI3N6ds2bL4+/tLHeWrtmnTJtavX4+Pjw/btm0rssrnt99+o2fPnuzdu5dWrVopR1y5u7tTu3Zt7t+/L1VsQRCEL4r4piwIgvAvBQYGAqChoYGNjY1y6ayRkRFNmjQhLCyM/Px8TExMGDduHBcvXmTXrl14eHhImPrLlpeXx4kTJ8Q84ffUq1cv4uPji+UGSm+zbt06vL29GTRoEK1atQIgKiqKn376iZYtWypniv43BwcH9PX1cXFx4fbt28TExNClSxflz2UyGT179mT58uWf5Ry0tLQ4fPgw3bt3p3379kXmGQtCcebn50ePHj0YP348GzduLJYX31RVVdHR0QHA1dUVb29vsrKyqF+/Pr6+vigUiiLdtaNHj2bIkCFMnTqVnTt3AgVzxX19fVFTU8PKyopx48bx7NkzPD093/q4hRfX7t69y++///4Jz7CgMK+lpUXbtm0/6eN8adTU1OjRo4cYIVEMmJiYKFd0ZWdnAwVjXtq2bcvSpUsZMGBAkY5+Z2dnJk2aRPv27ZUb1gmCIAj/nOx9uoJkMpmipHUPCYIgfC516tTBxMSEw4cPA5CTk4OGhgYAM2bM4MmTJ+zcuZPc3FxCQ0PFjNvPIDw8HEtLS1JTUylfvrzUcUoEU1NTbGxsWLx4sdRRyMzMRFtb+53HLFmyBD09PcaNG4dCoeDcuXNEREQgk8lwdHSkVq1afyn6QMEFAxcXFx4+fIhCocDd3Z3KlSt/ytP5W3K5nClTprB161YOHjyInZ2dpHkE4V0OHDjA4MGDmT9/PrNnzy5xYwsyMzPR0tIqkjs4OBhXV1diY2NJT0+nX79+ODo6Mnz4cOUxt27dwtDQEB0dHWWx+W3i4+OxtbWlVatWrF69+pNtIjt06FDS09M5cuTIJ7n/L9nRo0cZNGgQT548+dv3G+HTK3y/lsvl1K1bFxsbGzZt2lTkZ3l5eaipqQEFY1MGDBiAt7e3aAAQBEF4C5lMhkKheOcHteJ3WV8QBKGEKNycxtraGh8fH2bPng0UdAwXdjuYmJgovzw2adIEb29vacJ+ZQIDA/nuu+9EQfgD2NvbSz5XOCQkhKpVq+Lp6UleXt47j01KSiI8PJz169czc+ZMgoKCgILZorVq1QJ4Y7FKTU2N7du34+HhQXBwsOQFYQAVFRXWrl3LnDlzcHR0fGcXoiBIadeuXQwcOJCVK1cyZ86cElcQBtDW1v5L7p9//plZs2YBoKOjw9ixYwkODiYhIUF5TJMmTTAyMnpnQVgul+Pt7Y2dnR0qKiq0a9dOWRDOzs7m6tWrrFq16m9f395Hfn4+gYGBYnTEP9SlSxfkcjknT56UOorAf96vW7duTY0aNZQFYblcruy+LywInz59mlKlSpGTk0N4eDiZmZnShBYEQfgCiE5hQRCEf+nYsWP4+fkRGxtLkyZNlB9kAY4fP87o0aPR09OjW7durFu3TsKkXw8zMzNsbW1ZuHCh1FFKjDt37tC4cWN+//136tWr91ke8+nTp5QtW1a59HzQoEE0a9aMKVOm8Pz5c3R1dd+6LP3Zs2ds3bpV2X3fsGFDrKys/raDr7jbvHkz48ePZ+3aFmPuKwAAIABJREFUtUyYMEHqOIKg9NNPPzFhwgS2bNnCmDFjpI7z0cjlcpKSkv4yO3z27NmEhoayadMmvvvuuzeuPPhfo0ePRkVFhYoVK6Kqqkq7du1o27YtV65cwcvLi+3bt9OnTx88PT1RKBTcvn2blJQUOnXq9MG5L126ROvWrUlKSsLIyOiDby8UXESsWLEiO3bskDqK8P9iY2OV459yc3OLbF55/PhxoqOj2bp1K5MnT6Z27dqiS1gQBOEdRKewIAjCJ/C/HT65ublkZmYSGBhIZGQkM2bMoF+/fjx69Ag9PT1SUlIYMWKEKAh/JikpKVy7dk18UfhADRs2pF69ep+lWzg5ORk3NzccHByYOXMmoaGh5OXl8fz5cypVqgRAVlbWO+9DX1+fWbNmERoayrRp07C1tUVHR6fEbZb3v9zc3NizZw9Tp05l3rx5Jf58hC/DihUrmDhxIh4eHl9UQRgKOvWNjY2Vz7XCP5cuXYqdnR3Hjx8nJyfnnQVhhULB4sWL0dbWZvPmzXzzzTdkZ2fTpEkTABISEjAxMaFjx4707t0bKChGJycnM2LECGrVqsWIESMICwt779xGRkYsXbpUFIT/BTs7O44dO6bcxEyQXmFBOCcnR1kQTk5OZvLkyaxatQptbW2OHTvG2LFjlZ/zClfu/d3nBkEQBOGvRFFYEAThA0yaNImRI0fy008/ERcXB4CNjQ0ZGRloa2uzdetW5c+MjY1p27Ytx48fZ9q0aRIn/3qEhIRQrlw5vvvuO6mjlCgymQx7e3t8fHw+yf17e3uzd+9eAE6ePEleXh47d+5EU1OTH374ATU1NTIzMzE0NEShUGBkZMSpU6f+9sv61q1bWblyJcB7dfIVfnkszpycnPD19WXVqlWMHz++RGQWvkwKhYK5c+cyd+5cDh48yODBg6WO9FEtXbqUEydOACjnmf43BwcH/P39i8wWfpPw8HDmz59PmzZtSEtL4+nTpzRo0AB9fX0AHB0dqVmzJq9evcLCwgIo2Aivc+fOxMfHc/bsWQwNDbGyslLOdU9MTHznY1arVo3p06f/o/MWCvTo0YMnT56IDcuKocK9OebOncvixYu5d+8eBw4cYOzYsTRu3LjIsbdu3SI4OJihQ4fi7u4uRVxBEIQSSxSFBUEQ3pOTkxN37tyhS5cuBAQEEBAQAEB6ejpyuZx9+/YxZMgQxowZw7Nnz1i/fj2AmPf3mQUGBtKlSxflTtXC+7O3t+fy5cs8evToo9zf2bNncXZ2pmbNmri6urJq1SoArl69Sq9evahduzYDBgygfPnyyOVyatWqxZUrV5Sdeq9evSIlJeWd3bJjxoyhQ4cOyOXyNxaEs7KyiIqK4s8//wT+2ulfXNna2hISEsLu3bsZMmQIubm5UkcSvjIKhYIpU6awatUqfH19cXR0lDrSR5eSkoKLiwsTJkwgMTERFRUVZDKZ8mJU/fr1uXbtmnLPgLcxMzNj69atbN++ncaNG7Nq1SoeP34MFPw7ZmRkcOHCBWrXro2+vr7yNS0vLw+FQkHVqlWpXLkyI0eOZOjQocTGxtKtWzcMDQ1xdXXl+vXrf3lMFRUV5YxV4Z+pWLEirVq1ws/PT+oowlvo6urSqlWrIpvCpqenc/LkSVxdXXFxcWHgwIHMmTOH6OjoIuMmBEEQhL8nisKCIAjvITU1lYyMDPz8/HBycmLkyJFs3LiRZ8+eYWhoSLNmzRg1ahT9+/dn7dq1BAYGUrduXaljf3Xy8/MJCQmha9euUkcpkb777juMjY3x9fX91/d1+vRpRowYQdWqVQkLC2PPnj04OzsDBRv8tGnTBoBNmzZhZGSEiooKffr04dy5czx8+BCAbt26/W2HnoqKCmZmZm+cPaxQKNDS0iImJgYnJyeMjY1Zvnz5vz63z8XS0pIzZ84QEhKCvb292ExH+GzkcjkuLi78/PPPBAQEYGNjI3WkT6JJkya0aNECHR0dBg4cyM8//wwUdPEqFAqys7ORyWQ0aNDgnfejpaXFmDFjOHXqFEuXLqVBgwbExMTw+PFjZDIZaWlpXLlyRfnepFAoUCgUqKmpkZ6ezi+//IKXlxdbtmzB0NAQT09PxowZQ1BQEBoaGvzwww8kJSV98n+Pr1GvXr1EUbgYmzZtGk5OThgYGKBQKHj+/DmtW7dmw4YNmJmZMXHiRGbMmEGvXr2YMGECAwcOlDqyIAhCiSKKwoIgCO+hQoUKVK9enc2bNyOXy2ncuDH6+vosWbKEBQsW8Pz5c4KDg5k5cyZyuZy6deuKDmEJXL16lbS0NKytraWOUiKpqKgwatQoypQp80G3k8vlbN68GQsLC/bs2cOLFy9o37499+7dY9GiRVSpUoUTJ04ol0Pb2Nigp6dHVFQUwcHBpKamMmDAALp06UK1atXw9vbm4cOHPH78mIcPH5KcnPzBs3Xz8/OVncMZGRlAQfetra1tiRrH0KxZM86fP8+tW7ewtrbmxYsXUkcSvnB5eXkMGzaM/fv3ExISQocOHaSO9NEVvga0bNkSdXV1xo4dy/fff09AQAB9+vThypUryGQyNDU1Af52LE3h61NKSgqpqan07t2bbdu2YWhoCBRsnvXf700qKirK26xcuZKoqCiWL1+Ompoax44dIzw8HFNTU0xNTVm/fj1BQUHKeesZGRns3r2bkydPipnjH4GdnR0xMTFER0dLHUV4i8KVXzKZjLJly+Lk5ERUVBQmJiaUK1eOe/fuoaGhQb9+/ZDJZCQmJnLv3j3u3btHfHy8xOkFQRCKN9n7fJiQyWQK8aFDEISvTXp6Ojdv3kRVVZWWLVty+fJldu7cybVr1wCYMmUKdevW5dSpUzx8+JCNGzcqv6C9qWtR+PQWLlyIn58fkZGRUkcp0bKystDS0nrv4yMiIpg+fTrTp08nKCgILS0t5s+fj46ODtnZ2airq7N582ays7OZOnXqGx+vcePGrFmzhvbt2yvnlqqrq1OlShXlKJb3IZfLlc+/zMxM5s2bR2RkJEOGDKFz587K5aclzcOHD+nSpQva2toEBQVRoUIFqSMJX6CcnBycnJyUHerNmzeXOtIn9/LlS3R1dcnMzCQ+Ph4fHx9CQ0OVr0mqqqp/WxT+b48fPyYjI4Pq1asDBa9DK1asIDk5mW3bthV5jbp16xa2tracPXuWmjVrAjBs2DBiYmJISEigYcOGbNq0ifr16wNw/vx5Vq5cycWLF1m0aBEuLi7Kx83MzERbW/tj/bN8VRo0aICzszMzZsyQOorwDv+9b8DevXuZMmUKnTt3pnXr1vTt2xdDQ0PS0tJwdXUlKSmJO3fu0K9fP7Zs2SJxckEQBGnIZDIUCsU7P8SIqoUgCMIbPHv2jKZNm7Jr1y5WrFjBpEmTqF+/Pps2bWL48OE0adKEAQMGYGZmRrly5bh8+TI5OTmoqKiIgrCEAgMDlbtRC//cmwrChRc8IiIiGDhwIAcOHFDOzAwMDAQKOnGnTZuGtrY2/v7+AGhqaqKiosL58+dp2LCh8r5evXrFkydPlI/XoUMHIiMj0dXVZefOnYwcOZImTZowadKkD8pe+PwLDw+nT58+PHnyhMWLF+Pg4KAsCJekTuFChWM4VFVVsbS0FN1PwkeXmZmJvb094eHhnDlz5ostCK9atYq5c+cydOhQbt26pfx7bW1tGjRowLhx45gxYwYVKlRATU3tgwrCAIaGhsqCMBQU2hMTE2nXrh3wn7nmV65c4ZdffsHFxYWaNWsq5xhPmTKFsLAw4uPjMTAw4OzZswDk5uaSmJhI9erVcXR0VI7gkcvlHD16lGHDhmFkZMSSJUvEqJkPZGdnJ0ZIlAD/X9wAoHv37tjb2+Pn54eqqqqyK9/AwIAFCxbQqlUrevfuzYYNGwD+dtNaQRCEr5WoXAiCILzBmTNnqFevHjt27MDHx4eMjAwWL15MYmIiFhYWqKurExISQk5ODtHR0chkMuUSdUEaaWlpXL58WcwT/kRkMhnx8fFMnjyZGjVqkJycrJz3a2BgQMOGDcnLy6NatWo0bNiQqKgoHjx4oLx92bJliYiIUN5XUlISw4YN4/Dhw+zbt4+4uDhGjhwJQPny5enRowfz588vUlx5X/PmzWPu3Lm0b9+e+fPn06JFC+VIjC1btjBv3jzGjh1b4nYpL1++PKGhoVSuXBkLCwux3Fn4aNLT07G1teXWrVucO3eOxo0bSx3pk9i8eTN79+5l8ODBNG7cmFmzZrFhwwauXbumvFhUpkwZOnfu/NG6RvX09Pj555/p168f8J+l8BMnTqRs2bLK11FVVVVu3bpFvXr1kMvlqKqq0rFjR/bu3QsUrJzo27cvRkZGGBgYUKtWLQDu3buHv78/HTp0wMvLi4MHDxIaGvpRsn8t7OzsuHTpEsnJyVJHEf6GTCYjNzcXHx8fDAwMOHDgAOXKlVP+PCUlhf3793Pnzh0WL16Muro6+fn5hIeHs3DhQgmTC4IgFE+iKCwIgvAGBgYGJCYmKosumzdvRlVVlZ07d6KmpoaOjg5Lly7F3Nyc/fv34+7ujr6+vsSpv24hISHo6urSunVrqaN8EV6/fo2Hhwfu7u6kpqYCcOPGDTIyMli6dCkTJ04kKyuLS5cuoaOjg5aWFvfu3QMKNm+SyWTcvHkTKOiUq1WrFrq6usr7r1evHgMGDGDfvn1s2bKFUaNGUbVq1Y+SvVSpUkycOJFRo0ZRo0YN1NTUiIyMxNramlOnTpGTk4OxsTGzZ8/m+PHjH+UxPxddXV0CAwNp3rw5lpaWXL16VepIQgn3/PlzunTpQkJCAmFhYV/0JqmPHj2iW7du1KtXj2nTpvHzzz+jUChYsWIF3t7eyuNkMhlqamof9bELO45VVVXJy8ujZcuWTJgwgYoVKyqPGTFiBFeuXEFdXR2A4OBg5egIgOjoaJKSkqhdu7byQlflypXZtGkTY8aMoUOHDlSoUIHff/8doMjM4XPnzvH69Wsxh/gNWrZsiaGhYYl7P/haFV4g6d27N7a2tvTt2xeAp0+f4uXlRXh4OGvXrqVChQrKCywmJiZ4eXkxbNgwacMLgiAUMx/3044gCEIJ9uTJE1RVVcnJycHExIQqVapw+/Ztateujbq6OvPnz2fo0KEcOnSI1atXExERQWxsLJ07dy7ypU6QRlBQEJ06dfroX+S/VsuXLycxMRGZTMbOnTvx8vIiNTUVS0tLnjx5Qvny5enfvz8hISG0bNmS69evc/fuXRo0aEDNmjVRKBQ8ffoUAA0NDeV8TPjPXMCBAwfSp08fNDQ0Pmr26dOno1AolB15Pj4+bNiwge7duzNgwADKli2Ljo4OpUuXJi0t7aM+9uegpaXFkSNHGDlyJO3bt+fYsWO0b99e6lhCCfTkyROsra3JysoiLCxMuZnZl6pz585MmzaN8+fP06pVKypVqsS8efMIDg5m+PDhyOVynJycPnkONTU11q5dW+TvFAoFnTp1wsvLCz09Pby8vLh48SLBwcHKY27duoVcLqdp06bK2+jo6BT5uYqKClWqVAH+U4jesGEDkyZN4vbt28rXYeE/VFVV6dmzJ35+fowaNUrqOMJ70NHRoVmzZsr/n56ezrFjx/Dz82Pp0qXUr19fOb87Pz8fXV1dfv/9d7p164adnR2+vr4fPBpGEAThSyQ6hQVBEIDr16/j5OTEqFGjsLGxITU1FWdnZ9atW0dkZCQ5OTmUKlWKFStWsHv3buLi4mjdujWDBg0SBeFiQC6XExQUJOYJfyRXrlzhzp07zJ07l61bt1KnTh1lx1pMTIzyi1T37t2Ji4tDT0+PqlWrcvr0aV69ekWpUqWIjIzk1atXRe63cAPA//4i9rELwlAwV7iwIJydnc358+dxc3Nj7NixVKlSBR0dHeLi4vD396d27dof/fE/BzU1NXbt2sWIESPo1q2bmIcpfLCkpCTatWuHXC7nzJkzX3RBODs7m/z8fL777jvatGnDhg0buHLlCtnZ2QBYW1szcuRIEhISJMsok8n4/vvvSUpKol+/fqSlpbFjxw4aNWoEFLzPRUdHU65cuSKF3cJZqWFhYUyfPp06depgb28PFHQWb9iwgdmzZ2NqakrDhg1Fp/Bb2NnZcfLkSdLT06WOInygvLw8jh8/jru7O5MnT8bc3ByFQoGKioqyU7jw937Lli1cvXqV9u3bi+eCIAgColNYEISvnFwuRy6Xs2/fPvr06UPnzp3x9vZmzZo1bN26lbi4OObMmYObmxu1atVCVVWVevXqFVkGL0jvxo0bpKamYm1tLXWUEq2wgzc3Nxdra2tq1qwJQFZWFvXq1aNFixY8ffqUe/fuoa+vr7wg8scffzBy5EhcXV0ZMWIEo0ePRkVFRXl7gNWrV6Otrf3Zz+no0aMEBgYyadIk5eMfP36crVu34uDggKWl5WfP9LGoqKiwbt06DAwMcHR0ZNeuXQwZMkTqWEIJkJCQQMeOHSlfvjyBgYGULVtW6kif1JIlS7CwsMDa2pr169ezbNkyxo8fj62tLSYmJrx8+ZKEhAQ0NTUlzVmtWjWOHj0KQEZGBqVKlVJ2O0ZFRZGYmEjr1q2Vr2UymQxVVVUOHjzIhAkTmDlzJs7OzmhoaHD+/Hlmz57NkCFDsLKyokuXLgDKIplQVMeOHVFVVSUkJAQHBwep4wgfQE1NjdevX9OnTx969uwJ/OfzTOHms2FhYRw8eJD4+Hj69u3LkCFDkMlkvHz5UnymFwThqyY6hQVB+KqpqKigpqbGq1ev0NfXp0aNGjRq1Ig///yTR48eMWHCBKZNm8aZM2ewtrZmzpw5lCpVivLly0sdXfgvgYGBNGnSRLlkVnizZ8+e8cMPP7BhwwZlR1zh5krwnw7eVq1aKYuL8+fPx9vbm/j4eDQ1NenevTt+fn4kJSUBBUWMY8eOoa+vz6JFi2jYsCGLFi2iWrVqdO7cWXnfUhSEAV6+fEnPnj2pUqUKOTk5LFiwAB8fH1xdXRkzZgwPHjxgwYIFXLp0iRcvXkiS8d+QyWTMnTuXDRs24OzsrNxpXRDe5t69e1haWlKlShVCQkK+6IJwYSdgVFQUgwcPLnKx9/Dhwzx79oxr166xceNGGjRoUKw2oipVqhSAsqiVnp6OhoZGkRnD4eHhuLi4sGnTJrZv387EiRPR1NQkNDSUFStWMGvWLEaMGMGDBw+UK2kK708oSltbG2tra7HqooQaOXIkY8eOBSA3NxcVFRUyMjL4888/6dGjB56enpQtWxYPDw/WrVuHvr4+q1evplGjRpw6dUri9IIgCNKRvc+yCZlMphDLKwRB+NJs374dHx8fgoODWb9+Pc2aNcPKyoq5c+eybt06OnfuzJUrV7h69SpGRkbcvHkTLS0t6tWrJ3V04X9YWlrSunVrVq5cKXWUYis/P5+pU6cSHR1Nz5498fDwIDAwsMiu3W9y8uRJXr16xb59+9DW1mb79u3Mnj2buLg4li9fzvLlyzE3N+f7779X3iYzM1OyIvD/unz5Mi4uLlSuXJnr16/Tu3dvhgwZQsOGDdHS0uLMmTPs3LmTChUqkJiYyP79+6WO/I/t27ePYcOGMWvWLBYsWCDmJQp/cefOHTp16kSzZs04cuRIsXmefmo3b96kffv2dOrUCT09Pezs7GjatCm//vorzs7OqKurl5jnS2HncGRkJJ07d6Zs2bL4+fnRuHFjAC5cuMCYMWPQ0NDg2rVr3LhxAzc3N8LDw5Xdk8KbeXp6MnnyZFJSUsT+BCVU4e94Wloaa9asITU1FYVCwY4dO8jLy0NdXZ0zZ87g7u6OhYUFcXFx7Nu3jx9//PGzzBMXBEH4nGQyGQqF4p1v/KIoLAjCV2njxo2sXr0aIyMjduzYQf369cnNzUVHR4dbt25RvXp1dHV1GTt2LCkpKRw4cEAstyymnj9/Tvny5Tlx4oTYbOsdnj59Ss2aNYmKisLY2JilS5eiq6tLnz59MDIyemOxoLD4AAUjIszNzUlKSiI9PZ3x48eTnJwMgIeHR7GeR3r//n0SEhIoXbo0zZs3Ry6X/+ULf3Z2Nk5OTvz444/UrVtXoqT/nr+/P3369GHkyJFs2LBBdAUKSpGRkXTp0gUrKyu8vLwkH5Xwuc2dOxczMzN0dXU5dOgQfn5+dO7cGQ8PD6mj/WNHjx5l3759XLhwgbp16zJ//nw6dOjA5cuX2bRpE+fPnycjI4O6dety/vx58vPzxWeZd3jy5AkVK1YkNDSUdu3aSR1H+BeaN29OgwYNGD16dJH/ljExMQwYMICcnBwOHDhAo0aNOHfuHNOmTcPd3V1sxCgIwhflfYrC4puCIAhfnUWLFvHTTz/h7+9Pz549uXz5MpqamsodvJs0aaKcL9arVy+qV68uvkQVYydPnkRbWxtzc3OpoxRrmZmZWFlZcf/+fQC6du1KWloaly9ffuttjhw5ws2bNwGIjY3Fzs6O9PR09PT08PDwwN3dneDg4GJdEAaoWbMmVlZWmJmZcfjwYVJSUlAoFPj7+3P8+HFyc3NZv349v//+O/r6+lLH/Ve6d+9OcHAwu3fvZsiQIeTm5kodSSgGLl68SIcOHejWrRsHDx786grCAN988w3Lly+nffv2mJqaKi96bd++nYyMDKnj/SP29vYcPnyY+/fv4+LiwtOnTwFo0aIFe/bs4f79+5QqVQotLS1CQkKKfJYRDT9/Vb58eSwsLMQIiS/A8ePH8fLyUhaEFQoFGRkZGBkZsXLlShwdHbG1teX+/fu0bduW4OBgURAWBOGrJIrCgiB8VUJDQ3F3d+fEiRN8++23mJqacvjwYVJTU5XHREVFcfDgQU6dOsUPP/yAhoaGhImFvxMYGEjHjh3Ff6e/UaZMGSpVqkRiYiIAjRo1wsjIiKioKPLz84t0CRcWC37//XemT5+OlZUVI0eOpH379ujp6QEFV54rV678+U/kX4iPj2fWrFnEx8cjk8moUKECAwcOpHHjxkRGRrJw4cIvYl5427ZtOXPmDCEhIdjb25OZmSl1JEFCZ86coXPnzvTt2xdPT8+vYlm8XC7n/PnzeHt7ExkZCYCTkxMWFhYEBQWxceNG9uzZw5QpU9DW1lbO7y2pNDU16d+/P7179wZQXgwKDQ1FU1OTkydPFpnxDgWrR7p06cLOnTt59uzZZ89cXPXq1Qs/Pz9RNC/hjI2Ngf/sm5CQkMCaNWsoVaoUHTt2ZMGCBZiamnL27FmAIrPVxcVUQRC+JqIoLAjCV8Xc3JybN29SvXp1cnJyMDExoWrVqkWKJrq6uuzZs4edO3fSoUMHli1bJmFi4V0UCgVBQUF07dpV6ijFnq6uLhUrVuThw4e8evUKLS0tDA0NefnyJY8ePSpybOGX4VmzZrFt2zZGjRpFVFRUiZ+3l5aWhp6eHm3atAEK5qva2Njg4+ODl5cXjo6O5OfnS5zy42jWrBnnz5/n1q1bdO3atURuoif8e4GBgXTr1o2RI0eyffv2r2acyPDhwxk+fDg7d+5kyZIlhIaGAgUdtDY2NtSpU4dOnTrRqFEj5aaaXxJ1dXUUCgUWFhZ4e3sDRTcVVSgUZGVlUbt2bWbMmEHFihWxs7PjwIEDvH79WqrYxYKdnR3379/n9u3bUkcRPoLC1zxfX1+io6NRU1NTFn1zc3OVqwRycnLYs2cP/8fefQZEde0N278AsUTFgiCoWMEWC9YoFoqoFBERRUYR7MbYjS0WLNhi7zVRKTqK0rtgV1BsWLFXlGYDgoKUeT/4hPfknHPnqAE2DOv3KVGcuYJkGP577bUmTJiAjY0Nv/32m2TNgiAIxUnsKSwIQpkQFBREhQoVMDY2/o/bZn/88UdSU1Px8fEp+LWcnBzU1dWLO1P4Sjdu3KBt27Y8efKEhg0bSp1T4l2+fBkPDw9sbGzo3bs3ISEhrFu3jhMnTpCYmEh2drbSfx67d+9OixYtyM3NJS8vDxMTE0aNGgXAzZs38fPz4+XLl+jo6GBubk6PHj0kLv5nXrx4QZ8+fahUqRLh4eFoa2tLnSQUE19fXxwdHZk9ezZubm5l5oCxO3fu0LFjR54/f05aWhp79uzh+fPnHDx4EIDffvuNwYMHU61atVLzvf7Tp09FdhheTk4OUVFRyOVy/Pz8yM/Pp3///shkMiwsLMrkXTitW7dmyJAhLFiwQOoUoZD4+PiwaNEiTpw4QVpaGgcPHmTdunU8fvy44A6hHTt2cObMGd68ecPEiROxtbWVuFoQBOGfEXsKC4IgADKZjJUrV+Lj48OYMWPw8fEhNTW14PdXrFhBhQoVOHnyZMGvlYXba5VBeHg4zZs3V/pBZmExNDSkTZs2zJ07l8uXL5OQkEDFihUBWLBgAXfv3lX6W2bDwsLo3Lkzenp62NjYYGVlBUBeXh6tW7dm7NixHDlyhG3btinFqmE9PT3Onj2LqqoqPXr04Pnz51InCcXAy8sLBwcHlixZwrJly8rMQBjgu+++o1u3blSpUoUmTZowY8YMHjx4wLFjxwAYM2ZMwSC4NAyEnz17xs8//1xk28Coq6tjaWmJh4cHKSkpuLu78+nTJxwcHKhduzZjxozh+PHjSvF6+KVsbW3FvsJKxt7eHhMTE8zNzRk9ejRyuZyTJ0/+ZcsoNTU19PT0GDFihBgIC4JQZoiVwoIgKLWMjAwcHBzw8vJCU1MTf39/rl+/joaGBsOHD6dWrVqkp6ezbNkyNDQ0xKqQUsbU1BRDQ0M2bNggdUqJ8eHDB/z9/XF3d2fDhg3/9eCUSZMmkZKSwu3bt9m4ceN/7DVZVp05cwZ7e3s6duxIYGBgqRgYfan09HRsbW15+PAhkZGRNG/eXOokoYjs3r2bCRMmsGHDBqZMmSJ1TrFLT0/HxcWFKVOmYGxsjKqqKvPmzSMuLo7Q0FCSk5Oxs7PD39+/VKyc37JlC2vXruXp06fFOtxPS0vD398fuVxOVFQUWlpaODg4IJPJ+OFSZooaAAAgAElEQVSHH5T6QsOlS5fo3LkzL168oF69elLnCIXoxo0bVK9enXLlyv3lXARPT0/Onz9Phw4dGD16NKqqqmRkZHDp0iWSkpLQ19enc+fOEpYLgiB8PbFSWBCEMq9ixYo8fPiQ33//Hfh8gIixsTHp6el4e3uTmZmJhoYG/fv3Z9myZbx48ULpV0oqi4yMDM6dO4elpaXUKZJTKBScPXuWMWPGoKOjw48//ki9evWoUKHCf/163rp1K5s2beLKlStldiCsUCjIzc0t+PepU6diZmbGokWLCAsLQ11dveD3r1y5IlVmodHQ0CAsLIz27dvTo0cPpfhvEv7Txo0bmTBhArt37y5zA+H8/HwyMjLQ0NDAw8ODzp07F+wnOnv27IJ9tefMmUP79u1LxUAYIDg4mH79+hX7ELZatWq4uLgQHh5OYmIiCxcu5Nq1a3Tt2pUmTZowb948bt68WaxNxaVDhw7UqVOHwMBAqVOEQtamTRvq16+Pjo5Owa/5+/sTExND8+bNcXJyQlVVlezsbB49esT27dsJDQ2lS5cueHh4SFguCIJQNMRKYUEQlE5+fj7v378nKyuLOnXqsGfPHmJjYxkzZgw//PADAAEBAYSGhuLq6lpwQnFCQoJYEVKK+Pv7M3ToUN6+fVuwBUJZ8/jxYzw8PPDw8ODp06eYm5vj4uKCnZ0d3333XaE+V15eHioqKkp3UNXdu3ext7dHVVWV0NBQ9PT0gM+vI6qqqjx+/JiWLVty8+ZNDAwMJK7953Jzcxk9ejR+fn4EBgZiYmIidZJQSJYvX86iRYvw9PREJpNJnVPs5s2bR2xsLFWqVGH48OHY29sDny8AqaioMHz4cJ48ecKbN2+Ij4+XuPbL/PHHH2hqauLn51ew1Y3UXrx4weHDh5HL5Vy9epXvv/8eR0dHZDIZTZo0kTqv0EyYMIHHjx8TEREhdYpQhMLCwggPD0dHR4dx48ahqalZ8JoBn1fMV6tWDblczu7duwkICEBDQ0PiakEQhC8jVgoLglDmfPjwgXbt2jF79mycnJwICAjA1NSUypUrExUVxfXr14HP+8Vdv34dLy+vgj/753BYKB3Cw8MxNTUtcwPh9PR0fv/9d3r27EmTJk04fPgw48aN4/nz5xw7doxhw4YV+kAY4P79+0q3vUpiYiItW7bE2tqamzdvoqenV7BvpqqqKvn5+TRu3JjFixczYsQIaWMLSbly5di3bx+jRo3CwsJCrIRTAgqFgnnz5rF06VKOHj1aJgfCQUFBuLu7s3v3brp37878+fO5dOkSaWlpvHr1CgATExOio6NZtWqVxLVfLioqCjU1NUxNTaVOKaCnp8fMmTO5cuUKd+/eZfDgwXh5eRXcXr9hw4aCz3lpNmDAAE6ePFmwwlxQPtnZ2Vy+fBlVVVWcnJzQ1NQsuAD+53uBatWqARATE0PVqlXFQFgQBKUjVgoLgqBUtmzZwpkzZzhy5AixsbEsXLiQsWPHUqlSJU6dOkVubi7du3enZ8+eLFq0CCMjI5ycnKTOFr6SQqGgYcOGzJw5k8mTJ0udU+Ty8vKIiorCw8MDPz8/KlWqhEwmw8XFhY4dOxbLbcUXLlyga9euvHz58i/78JV2L1++LLgglJubS7ly5QpWCf/57/D5QtL333/PihUrpMwtNAqFgmXLlrFkyRL27t2Ls7Oz1EnCN8jPz2f69Ons3r0bf39/+vbtK3WSJFasWIGKigq//PILADNmzODdu3fA58OjBg8eTN++fTl48CBDhw6VMvWrjBkzhpSUlBJ/8UahUBAXF4dcLufQoUMkJCRgbGyMTCbD3t4eTU1NqRO/WnZ2NlpaWuzZs4chQ4ZInSMUkaysLF69ekXjxo3/4/fev3/PkydPWLduHRkZGTg6OuLo6FiwkliZ99UWBEE5fMlKYTEUFgRBqezevZsDBw4QHBxM1apViYuLY/369djY2KCvr8/t27dZsGAB7du35+XLl7i7u4sDl0qh+Ph4WrZsyYMHD9DX15c6p8jcuXMHd3d3vLy8SElJwcrKChcXF6ytralQoUKxtuTn51OvXj0WLFjATz/9VKzPXdTy8vJQKBQFA+A/fy0vL4+7d+/y7t07zp49i6urKw8ePFCqW6S3bt3K1KlTWb9+PVOnTpU6R/gKeXl5jB8/nsOHDxMcHIyxsbHUSZKJjo7Gzc2NoUOHYmJigouLC6NHj6ZVq1YcPXqUu3fvcuTIEakzv0p+fj5169Zl8eLFjB8/XuqcL5afn09MTAxyuRxvb2/evXtH3759kclk2NraUqVKFakTv9iQIUNQU1Pj4MGDUqcIxeDJkyccPXqU7OxsIiMjefnyJU2bNqVChQqsWrUKXV3d/1gp/OdFZEEQhJJIDIUFQSgTsrKyqFixIgqFguvXr+Pq6sqiRYto3749KioqxMbGMnfuXGbMmEG/fv1ISkqiYsWK5OfnU7NmTanzhW+wfv16tm/fzsOHD6VOKXSvX7/m0KFDuLu7c/nyZdq3b4+LiwsymQwtLS1J23766ScePHhAZGSkpB1FJT09nbdv33Lx4kU8PDzQ19cnODgYe3t7MjMz6dq1K5aWlqVy1dvfOXDgACNGjGDevHksXrxYrH4qBXJycnBxcSnYD/PP/fLLMj8/P4KCgjh37hwNGzbk2LFjAJw7dw4XFxd8fX1p27atxJVf7sqVK3Ts2JEXL16U2vMOcnNzOXHiBHK5HF9fX3JycrCxscHR0RFLS8sSv/3TwYMHmTBhAqmpqZQvX17qHKGI7dy5k59++onJkycX7Enes2fPv3zM69evSU1NJSgoiKCgIGrUqFHiV/ILglB2iaGwIAhKLz4+Hjc3t4Ih7/79+5k6dSpv3rxh/fr11KpVC1VVVTw8PFi9ejVnz56levXqYuhRyvXp04dmzZqxZcsWqVMKxadPnwgNDcXd3Z2QkBA0NTVxcnLCxcWFVq1aSZ1XICoqCktLS5KTk5XugsqzZ88YO3YsDRo04O7du4wbN45WrVqhp6dHrVq1gM8DjufPn6OtrV2qVrt9ieDgYAYPHszYsWPZuHGjWPlUgmVnZzNkyBCio6OJjIwsVYPOwubl5cWJEydo27YtEyZMoHz58jx58gQ3NzdsbW0xNDRELpezb98+7t27J3XuV1myZAn+/v5cu3ZN6pRCkZWVRVhYGHK5nKCgICpUqICdnR0ymQwzM7O/3KlRUrx//x4tLS1CQ0Pp3bu31DlCMXB1dcXHx4fIyMiCrbJu375NXFwc58+f5+TJkwwcOJDNmzdTtWpVTp06RdOmTSWuFgRB+O/EUFgQBKWlUCj4+PEjkydPxtDQEH19ffbs2YOZmRk//fQTgwcPRktLi3HjxtG8eXNu3rzJunXrOHjwYIn8wUP4cpmZmdSsWRNfX1+sra2lzvlmCoWCq1ev4u7ujlwu548//mDAgAE4OzvTu3fvEvl1mpOTQ+3atdm4caNS7kEbFBRElSpV6Nat219WheXn55OcnExAQAA7d+6kffv2DBs2jF69eklYW/jOnDmDjY0N/fv3Z+/evairq0udJPybDx8+MHDgQG7evElUVBQtWrSQOkkyu3fvZuPGjUydOpVNmzZhYGCAt7c3FSpUYOPGjdy4cYNbt27x9u1bfvvtN0xMTKRO/iqdOnWib9++LFu2TOqUQpeRkUFAQAByuZxjx45Rs2ZNBg8ejEwmo2vXriXqopS5uTnNmzdn69atUqcIxWTNmjVs2bKFkJAQtLS0GDp0KK9evcLT05O6deuSnJzM4MGD8fHxoW3btn85f0AQBKEkEUNhQRCU3rBhw5g0aRJdu3Zl586dPH78mNWrV5OXl8eSJUtISEggLi4OAwMD8vLyOHTokHjjVsqFhIRgb2/PmzdvqFy5stQ5X+3Vq1d4eXnh4eHB7du36datGy4uLgwePJjq1atLnfc/ubi4kJ6ejp+fn9Qpxe7Tp08Frymenp54e3sr3eD06tWrWFhY8MMPP+Dt7U2lSpWkThL+n4yMDPr168ezZ884fvy4Uu1t/bXy8vLo2bMnixcvpnfv3nz8+BF7e3ucnZ25ceMG7du3p1y5clSrVg0NDQ06dOggdfJXSUpKQldXl5iYGLp06SJ1TpF68+YNPj4+yOVyTp8+jZ6eHo6OjshkMtq2bSv5nV1bt25l9erVPHv2TPIWoficPn0aAGNjY44dO8bQoUMJDQ2lefPmdOzYkaVLl+Lo6Cj2FBYEoUT7kqGwmIwIglAqKRQK0tLS0NbWLri1+/LlywQEBBAXF0ft2rXx9PTk/fv3BAcHU7NmzRK7+lL4OmFhYfTs2bNUDYQ/fvyIv78/7u7uREZGoqenh7OzM/7+/qXuoDw7OzuGDh1KZmZmqfo7+BoKhYIPHz5w8eJFOnfuTJUqVYiLi8PQ0JDGjRtz7do1KleurJQ/CLZv355z585hbm6OhYUFgYGBVKtWTeqsMu/du3dYWFgUHHqop6cndZKk1NTUsLGx4enTp7x69QptbW0ePHhAZmYmeXl5bN++nYkTJ2Jqaip16jcJDQ1FS0uLTp06SZ1S5DQ1NRk3bhzjxo3j5cuXeHt7I5fLWb16Nc2bN0cmkyGTyTAwMJCkr3///kyePJlr167Rvn17SRqE4vevB3f26dOHkydPYmVlRW5uLmvXrsXR0RGFQqGU7wMEQShbxKuYIAilkoqKCtWrV2fixIkFh28ZGRlx8uRJ9u/fz/Xr11myZAnVq1fHyckJKysrpVvRV1aFh4djaWkpdcb/pFAoOHv2LGPHjkVHR4dx48ZRp04dTpw4wePHj1m6dGmpGwjD5x+OACIiIiQuKToqKircunWL3377jfz8fAA2bNhAu3btmDp1KqNGjaJ169aoqalJXFo0mjZtyvnz50lJScHU1JSUlBSpk8q0P/8ePnz4wJkzZ8r8QPhP1tbWXLp0iSVLltC6dWuMjIwYPXo0v/76K+3bt+fw4cNkZmZKnflNgoODsbS0VNrXmP9L3bp1mT59OrGxsTx48IBhw4Zx+PBhmjZtSocOHVi7di0vXrwo1qb69evTrl07AgICivV5hZLhz7ulW7duTZ06dUhNTSUtLU3iKkEQhMIjhsKCIJRq+vr6Bbfcjxo1ilatWlGnTh2mTp1K1apVJa4TCtuDBw949OgRFhYWUqf8n548ecKSJUvQ19fH2NiYp0+fsm3bNpKSkti7dy/GxsalemXJd999h6WlpdJvH3H16lVyc3PR0NAAPq8W6969O/Xq1eP48ePMmTOH1NRULl++rJQ/IOrp6XHmzBlUVVXp0aMHz58/lzqpTHr58iXGxsaUK1eOU6dOoaOjI3WS5G7cuMGBAweoWbMmy5cvx8nJiZkzZ9K6dWvg8xAnKyuLtLS0Unk3Q3Z2NpGRkfTr10/qFEnp6+uzYMECbt26xfXr1+nTpw/btm2jfv369OzZkx07dpCamlosLba2tmIoXEb9uWXIrFmzaN68Obm5uQWriMV2IoIgKAOxp7AgCErj4cOHfPr0qeDAEgsLC+bPny91llCItmzZwrp163jy5EmJejOenp7O0aNHcXd358yZMzRv3hwXFxecnJyoV6+e1HmFzsvLi0mTJpGSkvKXA9mUyaVLl+jXrx+XLl0iLi6OCRMmsGjRIsaNGwfAtm3bOH36NJ8+fSI9PZ0TJ05IXFw00tPT6d+/P48ePSIyMpLmzZtLnVRmPH36lF69eqGrq0tISIjYxgNYvXo1Pj4+NGrUiKtXr7Jhwwasra1JTk6mf//+GBoakp+fj4+PD6dOnaJNmzZSJ3+1yMhIrKyseP36tfg7/zcKhYILFy4gl8vx9vbm9evX9O7dG5lMxoABAwou4hW2uLg42rVrx+PHj2nUqFGRPIdQeuTl5ZW5VfyCIJRO4qA5QRCUQl5eHioqKv9zdWVkZCQLFiygYsWKNGrUiP379xdPoFBsrKysqF+/Pjt37pQ6hby8PI4fP467uzt+fn5UrFgRmUyGi4sLnTp1KlFD68L27t07tLW1CQkJKdhOQhmtXr2amzdvcurUKVasWIGFhQUVK1bE29ub3377jXXr1mFkZISxsTF2dnZMmzZN6uQi8fHjRxwdHYmOjiY8PLzUHdpVGt2/f59evXrRvHlz/P39S+WK18L2+vVrvv/+e06dOkWLFi3Ys2cPt2/fZvz48bx58wZNTU127dqFpqYmrVq1ws7OTurkbzJ16lRu3ryptBeaCkteXh6nTp1CLpfj4+PDx48fsba2RiaTYW1tXaiHZCoUCho1asT06dOZOnVqoT2uUPooFIq/vL/70p9RBEEQpCCGwoIglHqTJ0/m48ePpKSksHTpUurXr0/NmjX/z49PSUlBXV2dGjVqFGOlUBw+fvyIpqYmcrkcW1tbyTri4+Nxd3fHy8uL5ORkrKyscHZ2pl+/flSoUEGyruLWt29fGjduzI4dO6ROKVLZ2dmoqakRExODp6cn2dnZPHjwAEtLSxYuXAjAiBEjCvYbVla5ubmMHj0aPz8/AgMDMTExkTpJad28eZPevXvTqVMnjhw5QsWKFaVOKhFSUlJYuHAhu3btAuDevXv06NGDSZMmsXfvXjp27IiXl1ep/nwpFAr09fWZOHEiM2bMkDqn1MjOziYiIgK5XE5gYCBqamoMGDAAmUyGubl5oZwpMWXKFG7duiWG9cJf5OTk4OLiwvLly8UqckEQSpwvGQqLS1qCIJRYx44d49ixY6xfv54+ffrg4+PDoUOHSEhI+I+P/fPClba2thgIK6kzZ86Qm5uLmZlZsT/3mzdv2Lp1K507d6Zly5YcO3aMWbNm8fLlSwICArC3ty9TA2EAOzs7/P39Cw5iU1YVKlSgXLlyeHp6UqlSJWbNmsXWrVuRy+Vs2bIFZ2dnEhMTlXrFNEC5cuXYt28fI0eOxMLCgsDAQKmTlNLly5cxMTGhZ8+e+Pj4lOoBZ2H7c2sAmUyGl5cXEyZMYNy4cbi6uvLo0SPu37+Pv7+/xJX/zL1793j8+HGZ30/4a1WoUIH+/fsjl8tJSUlh165dvH//HltbW+rUqcOECRM4c+bMP/p+NWDAAM6cOcPbt28LsVwo7XJzc0lJScHIyIjr169LnSMIgvDVxFBYEIQS6927d9SqVQsNDQ0mTZpEr169ePfuHQcOHCA5OfkvH6vMt+oLn4WHh9O9e/diO0Dw06dPBAQEMHDgQHR1dVm+fDk9e/bkxo0bXL16lalTp6KtrV0sLSWRra0tycnJXLhwQeqUIvf8+XMuXbpEr169aNWqFe3bt8fc3JxLly5hYGDAL7/8goGBAcp+V5WqqiobN25k3rx5DBw4EE9PT6mTlMq5c+cwMzPDxsaGgwcPKu1+3d8iMzOTihUrsmvXLlq0aMGtW7fQ0tIq2ONbRUWFGjVqcOfOHYlL/5ng4GD09fVp2rSp1CmlVuXKlZHJZAQGBpKUlMTKlSu5f/8+JiYmNGjQgJkzZ3LlypWvfr3u0aMHVatWJTQ0tIjKhdKoUqVKhISEYGxsTM+ePTlz5ozUSYIgCF9FDIUFQSixevbsiUKhwNfXFwATExNMTExIT08nKioK+DysuXnzppSZQjEJCwvDwsKiSJ9DoVBw5coVpk6dSt26dRkyZAjly5cnICCAFy9esHbt2oIT7ss6XV1dunbtip+fn9QpRa5+/frUq1ePAwcOcOfOHSIiInj//j3Lly9n4cKFmJiYUK5cub9cnFLWAbGKigqurq5s3LiRESNGsHnzZqmTlEJUVBR9+/bFycmJvXv3Uq5cOamTSoygoCCqVq3Kvn37AHB1dcXNzY1OnTphaWmJr68vu3bt4sqVK/z4448S1/4zISEhYpVwIapZsyZjxozh+PHjvHz5klmzZnH+/Hk6duxIs2bNWLRoEfHx8V/0WOrq6lhbWxMQEFDE1UJpU6FCBQ4ePIizszN9+vQp9XcsCIJQtoihsCAIJZa2tjZWVlZER0dz5coVALp160b9+vXx8PAAYOXKlbx580bKTKEYPHnyhHv37mFpaVkkj5+YmMiaNWto3bo1HTt25PLlyyxfvpykpCQOHTqEpaWlGNL8F3Z2dvj6+irtAPRfeXp6cvfuXWxtbXF3d0dLS4v8/Hzu379PWloasbGxJCUlcfXqVe7fv8+HDx+kTi5SkyZNwsPDgxkzZrB48eIy8TVQVIKDg+nXrx8//fQT27ZtEwcW/RsbGxs6derE6NGjsbKy4vLly6irqzNz5kwcHR0ZNWoUx44dY/PmzdSpU0fq3G/2/v17zp49K4bCRURXV5cpU6YQExPD48ePGTlyJH5+frRs2RJDQ0N+/fVXnj179rePYWtrS1hYGFlZWcVULZQWqqqqbN68mYULF2Jvb8+ePXukThIEQfgi4qA5QRBKtD/++INFixZRtWpVzMzM6NmzJ8+fP2fgwIGcOnUKdXX1MreXa1m0Y8cOli9fzosXLwptq5CPHz8SEBCAu7s7x44dQ09PD2dnZ5ydndHX1y+U51B2jx49Ql9fn+vXr9OmTRupc4rc8+fPSU9P58OHD4SGhhIdHU2FChVQV1fn7du36OnpceDAAeDzEHnYsGESFxe9oKAgHBwcGDt2LBs3bhQDza905MgRhg4dyoIFC3B1dRVbIf0bhUKBiooKAQEBhIWF0bFjR1atWoWjoyOxsbFs2rSJpk2bkpGRQfXq1aXO/UcOHz7M2LFjef36tdg6pBjdvn0buVyOXC7n8ePHGBkZIZPJGDx4MLVr1/7Lx2ZkZFCrVi38/PywsrKSqFgo6fbs2cOECRNYvHgx8+fPF6/rgiBI5ksOmhNDYUEQSrz379+zc+dOYmJiqFWrFlpaWpw9exZ/f39q1aol3myVAba2tmhpafHbb7/9o8dRKBScP38ed3d3vL29yc/PZ9CgQbi4uNCzZ08x0PoGbdu2xc7OjsWLF0udUqyePHlCq1atiI6Opm3btmRlZTFw4EA+fPjA6tWr6dy5s9SJxeb06dPY2Nhga2vL3r17UVdXlzqpVHB3d2f06NGsWrWKmTNnSp1TIuXn56OqqsqrV6+wsbHh999/x9DQkB9++IGrV69y9OhRbG1tpc4sFM7Oznz48IGjR49KnVImKRQKLl++jFwu5/DhwyQlJdGrVy9kMhl2dnYFFx0sLS2pX78+u3btkrhYKMn8/f1xdHRkzJgxbNq0CTU1NamTBEEog75kKCx++hUEocSrXr06P//8M25ublSsWBFNTU3WrVuHlpaWGAiXAdnZ2Rw/fvwf7Sf89OlTli5dioGBAT179uTp06ds3bqVpKQk9u3bh4mJiRgIfyM7O7sysa/wv8rPz6dRo0bMnz+fWbNmcfr0aTp16oSuri6nTp2ic+fOpKamkpWVRXZ2ttS5Rc7Y2JhTp04RERGBvb09Hz9+lDqpxNu+fTujRo1i8+bNYiD8N/58Xa5Tpw6TJ08mPDwc+Hyx2M3NjcmTJ7Nz504pEwtFXl4eoaGhYusICamoqNCpUyfWr1/P8+fPOX78OI0aNWLmzJnUrl0bOzs7vL29sbKyIjAwkPz8fKmThRJswIABHDt2DC8vL4YOHVom3gsIglA6iZXCgiCUWH/eNiqUbSdOnKBPnz68fv36q24PzsjI4MiRI3h4eHD69GmaNWuGi4sLTk5O6OnpFWFx2XLjxg3atm3Lw4cPadKkidQ5xeLP1YsATZo04e3btwQGBtKjRw8Adu/ezf79++nUqRMPHz4kJCREytxic+/ePXr37k2jRo0IDAykWrVqUieVSGvXrmXu3Ln8/vvvuLi4SJ1TInl5eXHhwgXq16+Pubk57du3586dO8yePZv79+/Tvn17Dh06BHweqJb2VXjR0dF069aNpKSk/9iyQJDWp0+fiIyMRC6X4+/vj0Kh4MOHD6xdu5bJkyeLrT6Ev3Xjxg0sLCxo0aIFfn5+aGhoSJ0kCEIZIlYKC4JQar17946bN2/y6dMnqVMEiYWFhdG1a9cvGgjn5eURGRmJk5MTtWvXZubMmbRs2ZILFy4QHx/PL7/8IgbChax169Y0bty4TK0WVlFRITExkaZNm2JgYICKigo9evRAoVAQHx+PXC5ny5YtbNq0iU+fPrFgwQKpk4tFs2bNOH/+PMnJyZiZmZGamip1UomiUChYvHgxv/zyCwcPHhQD4f9DZGQks2bNonHjxsTFxbFy5UoUCgUtW7Zk4MCBNGjQ4C+HOCnDXR4hISF07txZDIRLoPLly2NtbY2XlxcpKSns27ePGjVqMGfOHHR1dRk3bhwnT54kLy9P6lShBGrTpg3R0dEkJCRgampKcnKy1EmCIAh/UfrfRQmCoHTevHlDr169GDNmjFgpLBAeHo6lpeXffsyfA98GDRpgaWlJeno6np6eJCYmsn37dn744QfxtVREVFRUGDhwYJkbCuvq6rJixQrCw8NJSEhg8+bNtGvXjk2bNlG5cmU6dOgAwIgRI3j9+nWZudVYT0+Ps2fPAtCjRw+eP38ucVHJoFAomD17NqtWrcLPzw8HBwepk0qsI0eOsGrVKmbMmMHu3btJTU1l5cqVuLm5cf36daZMmULVqlXJyckBUIrX9uDgYLF1RCnw3Xff4eDgwMyZM9HX12ft2rU8e/YMc3Nz9PT0mD59OrGxsYg7bIV/1bBhQ86dO4eqqirdunXj8ePHUicJgiAUEENhQRBKlJSUFExNTcnPzyckJEQcWFTGvXjxglu3bv3X/YTfvHnDtm3b6Ny5My1btiQ8PJyZM2fy6tUrAgMDsbe3p0KFChJUlz12dnbExMSQmJgodUqxGjRoEADLli1j4cKFbNy4kSFDhpCWlkZQUBDR0dHs37+fpk2bKsVqxi+lpaXFyZMn0dHRoVu3bty9e1fqJEnl5+czadIktm/fLoZ/X6Bdu3aEh4dz+/ZtqlSpUvD/jra2Nvn5+dy6dQtAad4fPH/+nBs3bmBtbS11iqkkanAAACAASURBVPCFbG1tuXfvHt27dyciIoLExETmz5/PpUuX+OGHHzAwMGDBggXcvn1b6lShhPjz+2KTJk0wMjIiLi5O6iRBEARADIUFQShBXr16hbGxMeXLl+fEiRNoaWlJnSRILCIigtq1a2NoaAhATk5OwcBXV1cXNzc3evbsyfXr17l27RrTpk1DW1tb4uqyp0uXLtSuXZuAgACpU4pddnY2CQkJrFmzBhMTE0xNTTExMWHVqlWsWrWKjh07MmPGDIAytXpMQ0ODsLAw2rVrR48ePbhy5YrUSZLIzc1l1KhReHp6EhERgbm5udRJJd6ECRMwNDRkzpw5dO7cmYoVKzJv3jzGjx9PkyZN8PX1JSkpSerMQhMaGkqdOnVo166d1CnCF2rZsiVNmjQp+J6nra3NxIkTOXfuHE+fPmXcuHGEhITQqlUr2rRpw8qVK3ny5InE1YLUqlSpQlBQEGZmZvTs2ZNTp05JnSQIgiCGwoIglAwvXrzA2NiYGjVqcPz4cWrWrCl1klAChIWF0bdvX+Li4pg6dSp16tTBwcGBcuXK4e/vT0JCAmvXrqVNmzZSp5ZpqqqqDBgwoExtIfGnChUqkJ2dTXBwMB8+fAA+3/Gwfv16Dh06hJubG+vWrWPOnDkMGjSIwMBAiYuLT6VKlfDx8cHS0hJTU1NOnz4tdVKx+vTpE0OHDiUoKIgTJ07QvXt3qZNKtISEBA4cOEBycjI//fQTmzZtYsKECdSqVavgdms1NTU+fvyIjo6OxLWFJzg4GGtra6XYBqOsUFFRwdbW9r9eCG3QoAGzZ8/m2rVrxMfHY2dnx759+2jcuDFdunRh06ZNZe6uGuH/V758eby8vBg5ciR9+/bFx8dH6iRBEMo4lS9ZtaKioqIoS6tbBEEoXk+ePMHMzIz69esTHBxM1apVpU4SSoDnz5/TrFkzatWqRUJCAkZGRjg7O+Pg4ECNGjWkzhP+zbFjx7C2tiYlJaXM/f2kp6fTvHlzWrVqRXx8PC1btiQgIICKFSty+PBhLl++jK2tLYmJiUycOJH4+Hg0NTWlzi42+fn5TJ8+nV27duHt7U3//v2lTipyWVlZDB48mNjYWKKiomjdurXUSSVaeHg427ZtIyUlhUePHrF161YcHR0B+Pnnn8nJyeHly5fExMSwfft2BgwYIHFx4fjw4QOampocOnQIW1tbqXOEr3D27FlMTExISkr6n3e2KRQKrl69ilwu5/Dhw7x69QoTExNkMhn29vZl7num8Plr4tdff2X+/Pls27aNH3/8UeokQRCUkIqKCgqF4m+vOouhsCAIknrw4AG9evWiadOmBAQEULlyZamTBAl9/PiRgIAAPDw8CA8PR6FQMGPGDH788UcMDAykzhP+xqdPn9DW1mbr1q04OTlJnVPs4uPjiYuLQ0VFhW7durFmzRp0dXXx9PRk1qxZjBw5EoAhQ4YwduzYMreNgEKhwM3NjaVLl7Jv3z6GDx8udVKRyczMLNhz9Pjx4zRt2lTqpBJv2rRpGBkZ4eDgQFBQEPv372fhwoXk5eXx4cMHLl68SHZ2Ng0bNmTYsGFS5xaakJAQ7O3tef36NVWqVJE6R/gKeXl56OjosHr16oLX9y+Rn5/P+fPnkcvlHDlyhLS0NCwsLHB0dKR///7i66CM2bt3L+PGjWPhwoW4urqKOwYEQShUYigsCEKJFh8fT69evTA0NMTHx4dKlSpJnSRIQKFQEB0djbu7O97e3uTm5jJo0CBycnJ4/PgxMTExUicKX2j48OF8+PChTN8OmZWVhZWVFc2aNcPFxYX169djYGDA7Nmz8fLy4pdffuH8+fNlduXoli1bmDZtGhs2bGDKlClS5xS6tLQ0rK2tSUxM5Pjx4zRs2FDqpFJh4cKFpKens3HjRlJSUqhXrx42NjacP3+eIUOGsGHDBtTU1KTOLHQTJkzg6dOnhIWFSZ0ifIORI0fy7t07/P39v+nP5+TkcPz4ceRyOX5+fuTl5WFjY4NMJsPCwkIclltGBAYGMmTIEEaOHMmWLVuU8rVOEARpfMlQWOwpLAiCJG7evImxsTGdO3fGz89PDITLoKdPn+Lm5kbTpk3p0aMHjx8/ZsuWLSQnJ7N//37u3LmDhYWF1JnCV7CzsyMsLKxgb92y6OHDh+Tk5DBu3Di6dOmCt7c3fn5+ODg4sHv3bnx9fQsGwnl5eRLXFr/Jkyfj7u7OjBkzWLx4sVIdvvfmzRt69erFmzdvOHPmjBgIf4H09HSys7MZNGgQBgYGKBQKMjIymDFjBr6+vty5c4fY2FgOHDggdWqhUygUBfsJC6WTra0tx44d++bveerq6lhYWODu7k5ycjIeHh7k5uYyZMgQdHR0GD16NFFRUWXye0VZ0r9/fyIjI5HL5QwZMoSsrCypkwRBKEPEUFgQhGJ39epVTExMMDEx4ciRI2IlRBmSkZHBvn37MDExoVGjRgWHbTx9+pSoqCiGDx9O5cqVSUxMJC4uDktLS6mTha/Qt29f4PP+wmWVuro6t2/f5uPHjwBcv36dtm3bsnv3bq5fv06zZs3w8PBg3LhxuLm5SVwrDScnJ/z8/Pj111+ZOnUq+fn5Uif9Y0lJSZiYmJCbm8vp06epW7eu1EmlwuDBg5k3bx5t27alb9++qKqqoq+vz4oVKwDQ1NTEwcGBW7duSVxa+G7evElCQoIYCpdivXv3RqFQEBkZ+Y8fq1KlStjb23P06FFSUlLYtGkTr169wsLCgrp16zJlyhRiYmKU6kKa8P/r3r07Z8+eJSYmBktLS9LS0qROEgShjBBDYUEQitXFixfp1asXlpaWHDx4EHV1damThCKWl5dXMPDV0dHh559/pmXLlly4cIG7d+8yb9486tev/5c/ExERgaamJh06dJCoWvgWlStXpm/fvvj5+UmdIplmzZoxZswYRowYQa9evVi+fDlmZmY0aNAAgHXr1hEfH4+VlRUXL17kp59+krhYGjY2NoSHh7N//35cXFzIycmROumbvXjxAmNjY7777jtOnjyJtra21EmlwtatW7l//z43btwgIiICAwODghWR2dnZREdHs27dOtavX680B8v9q+DgYL7//nsaNWokdYrwjSpXrkzv3r0JCAgo1MfV0NDA2dmZsLAwEhMTWbRoEdevX8fIyIjGjRvzyy+/cOPGDTEgVjKtWrUiOjqaxMTEgkMMBUEQiprYU1gQhGJz7tw5rKysGDRoEHv27BF7Zim5u3fv4u7ujpeXF4mJiVhZWeHs7IyNjc3/XB3u6OiImpqaUt4yrOw8PDyYOnUqKSkpZfqij4eHB7du3cLMzIxHjx4BkJKSwtGjRzl27Bh169bl2bNnjB49mqNHj1K9enWJi6Vx9epVLCws6NKlC4cPHy51Wwk9evSIXr160aBBA4KCgtDQ0JA6qVRIS0ujVatWREVFkZyczLZt2/Dw8Cj43vDixQt27txJRkYGhoaGjBo1SuLiwmdkZESPHj349ddfpU4R/oG9e/cyd+5cEhMTi/x9bUJCAocPH0Yul3PlyhVatGiBTCZDJpOhr69fpM8tFJ/Xr19jbW3N69eviYiIEH+3giB8M3HQnCAIJcbJkyexsbFh+PDhbNu2DVVVcaOCMnr79i2HDh3C3d2d2NhYDA0NcXFxYejQoV+8ei43NxdtbW02bdrE8OHDi7hYKGxv376ldu3ahIWFYW5uLnWO5KKjo9m6dSstW7akXLlyeHt7ExYWxrt371iyZAkVKlRg//79UmdK6t69e/Tu3ZvGjRsTGBhYagar8fHxmJub06pVK/z8/Pjuu++kTio1rK2tqV+/Pjt27ODTp0+MHz+emjVrsnz5cipWrAhAZmYmlStXlri0aLx+/RptbW1Onz5Njx49pM4R/oGUlBR0dHQ4c+YM3bt3L7bnvX//PocOHUIul3P37l06duyITCZjyJAhYvsaJZCZmcmgQYO4evUqYWFhtG/fXuokQRBKIXHQnCAIJUJERARWVlaMGTOG7du3i4GwksnJySEwMJBBgwahq6vL0qVL6d69O9evX+fatWtMmzbtq26nvnTpEu/evSvYn1YoXWrWrImJiQm+vr5Sp0jqz4vpUVFRpKWlMXLkSObOncvQoUOxs7PD3NycOnXqMHfuXIlLpdesWTPOnTtHUlISpqampKamSp30P8XFxWFsbEynTp0IDAwUA+GvkJ+fT7t27di6dSsA5cuXZ+nSpSQlJf1lP3JlHQgDhIWFUb16dbp27Sp1ivAPaWtrY2RkVOhbSPwvTZs2xdXVlTt37nDt2jV69erF5s2b0dPTw8TEhF27dvHmzZtibRIKT+XKlQkMDKRPnz6YmJhw4sQJqZMEQVBSYjIjCEKRCg4Opn///kydOpUNGzagovK3F6qEUkKhUBQMfOvWrYuDgwNqamr4+fmRkJDAunXraNOmzTc9dlhYGB06dBD7cpZidnZ2+Pv7K8UBYt/qz9e69+/fA1C3bl3y8vJQUVFh+PDhXL58mcWLF1OrVi2mTJnChg0bOH/+vJTJkqpfvz5nz54FoEePHjx//lziov/bxYsXMTU1xdzcXByW+g1UVVVZtmwZampqBa8Renp62NnZMXPmTHbs2KH0rx3BwcFYWFhQrlw5qVOEQmBra4u/v78ke/yqqKhgaGjIqlWrePz4MefOnaN169a4urqio6ODtbU1Xl5eZGRkFHub8M+oq6vj7u7O2LFjsbS05MiRI1InCYKghMRQWBCEIuPr68vAgQOZO3cuK1euFANhJZCYmMi6deto27Yt7du3JzY2Fjc3NxITEzl8+DBWVlb/+Ifc8PBwLC0tC6lYkMKAAQNITEwkNjZW6hTJLVmyhNjYWAYPHszPP/9MfHw8zZo1Q0dHh6pVq5Kbm0vt2rV5//49ZmZmJCcnS50sGS0tLU6ePImOjg7du3fn3r17Uif9hzNnzmBubo69vT2enp5let/sb5Genl7wz7m5uX+5c2jQoEFs376d2NhYIiMjpcgrFjk5OURERNCvXz+pU4RCYmtry8OHD4mPj5e0Q1VVFSMjI7Zs2cLLly8JDQ2ldu3aTJo0CW1tbRwcHPD19SUrK0vSTuHLqaqqsnbtWtzc3HB0dGT79u1SJwmCoGTEUFgQhCJx6NAhHBwcWLx4MUuWLBED4VIsKyurYOBbr149Nm/ejK2tLffu3SM6Oprx48dTo0aNQnmu1NRULl++jIWFRaE8niCNOnXq0KVLlzK/hQRA1apVCQgIQEtLC4AOHTpgZGRUsBJSS0uL+fPns2TJEszMzFizZo2UuZLT0NAgLCwMQ0NDunfvztWrV6VOKhAREYGFhQUjR45k9+7d4rDUr/THH3/QsmVLVqxYAUC5cuVQKBR/WRXcpUsXqlatyuvXr6XKLHLnz58nIyNDfJ9TIk2bNqV58+bFvoXE3ylXrhy9e/dm7969JCcnI5fLUVFRwcnJidq1azNixAgiIiLIzc2VOlX4H1RUVJg9eza///47U6ZMwdXVVZJV6YIgKCdx0JwgCIXO3d2dUaNGsWbNGmbMmCF1jvANFAoFMTExuLu7c/jwYXJzcxk0aBAuLi4YGxsX2b7QBw4cYNKkSaSmporbaku5NWvWsHv3bu7fvy8uCn2BixcvMm/ePCZOnMjAgQOlzpFcTk4Oo0aNIiAggKCgIIyNjSXt8ff3Z8iQIcyYMYMVK1aIr+lvNHToUA4dOoSOjg579uzB2toa+LxqWE1NrUx8XmfOnMnFixcLtksRlMPcuXM5deoUFy5ckDrlb/3xxx8EBAQgl8uJiIigRo0aDB48GJlMhpGRkTj3o4QLDg7GwcGB4cOHs337dnFxUhCEv/UlB82JobAgCIVqz549/Pjjj2zevJmJEydKnSN8pWfPnuHh4YGHhwePHj3CzMwMZ2dnBg4cSJUqVYr8+Z2cnPj06RPe3t5F/lxC0Xr48CEGBgbcvHmTVq1aSZ1TIuzdu5c1a9bQsWNH4PMP502bNiU2Npb69euTmZnJsmXLaN68ucSlJUN+fj7Tpk1jz549eHt7Y2NjI0mHXC5n+PDhLFmyhPnz50vSoCyCg4OBz1sRTZs2jZ49eyKXy6levTrw+c6UihUrSplY5Fq0aMGIESOYM2eO1ClCIbpw4QJdu3bl1atX6OrqSp3zRd6+fYuPjw9yuZxTp05Rr149HB0dkclkGBoalomLNKVRdHQ0/fr1w8TEhIMHDyr9a6YgCN/uS4bC4lKgIAiFZuvWrfz444/s3LlTDIRLkYyMDPbv34+pqSkNGzbEy8uLkSNH8vTpU6KionB2di6WgXB+fj4RERFiP2Eloa+vT6tWrcQWEv+iVatW3Lt3jzp16jBo0CDatm2LlpYWw4YNw9HRkc2bNxcMhPPy8iSulZ6qqiqbNm1i7ty52NnZ4eXlVewNv//+O05OTqxdu1YMhAtBs2bNyMzMZOzYsaSlpVGtWjW0tbVZv349gNIPNx4+fMjdu3fFfsJKqHPnzujo6BAUFCR1yherWbMmY8eO5cSJEyQkJDBjxgxOnz5N+/btadGiBUuWLOH+/ftSZwr/xsjIiHPnznHp0iX69u1bcKCtIAjCtxArhQVBKBTr1q1j9uzZ7Nu3D2dnZ6lzhP8hLy+PkydP4uHhgY+PD+XLl8fR0REXFxd++OEHSVaHXLp0ic6dO/Py5Uvq1KlT7M8vFL5FixYRGBjItWvXpE4pMVasWMHZs2cJCwuTOqVU2bx5M9OnT2fjxo1Mnjy5WJ9zx44djBs3rlieUxk9evSImzdvUrduXQwNDQtud/7zNvXz588zcOBAdHV1uXr1qlLfvr5p0yY2bNjAkydPxCpMJTRu3DhevnxJSEiI1Cn/yKNHjzh06BByuZzbt2/Trl07ZDIZjo6O6OnpSZ0n/D8vXrygb9++qKurEx4eXmpWqAuCUHzE9hGCIBSLFStW4OrqipeXF46OjlLnCH/j3r17uLu74+npSWJiIpaWlri4uNCvXz/JV2i5ubnh4+NDXFycpB1C4YmLi6Ndu3Y8fvyYRo0aSZ1TYnTv3p0+ffrg6urKlStXiI+PJzMzk+TkZCpVqkR6ejpqampcv36dVatW0axZM6mTSwRPT09GjhzJwoULcXV1LdKh2qpVq1iwYAH79+/HycmpyJ5H2d26dYu5c+fy9u1bqlatiqOjIyNHjvyvH3vt2jXatWtXzIXFq0+fPjRt2pStW7dKnSIUgZCQEAYOHMjr16+pWrWq1DmF4ubNm8jlcg4dOsSTJ0/o3r07MpmMwYMHFxygKkjnzZs39OvXj6SkJI4dO4aBgYHUSYIglCBi+whBEIqUQqHA1dWVxYsXc+TIETEQLqHevn3Ljh076NKlC82bNyc0NJQZM2bw8uVLgoKCGDRokOQDYYCwsDBxGruSadu2LQ0bNsTPz0/qlBLl0KFD1KhRA4DTp0/j7OxM5cqV+fDhA40aNSI4OJj9+/ejrq4uThj/F8OHD8fPz4+VK1cybdo08vPzC/05FAoFCxYswNXVFW9vbzEQ/oeCg4MxNTUlOjqaadOmsXLlSiIjI4mKiuLy5cvA50PmAKUfCGdkZHDq1CmxdYQS69WrF+rq6kREREidUmhat27NihUrePToETExMXTo0AE3Nzd0dXWxsLDA3d2dtLQ0qTPLLE1NTaKiomjZsiXdunUreF0VBEH4UmKlsCAI30ShUDB37lw2btyIj4+P+CGnhMnJySE8PBx3d3eCgoKoUaMGw4YNw9nZmbZt20qd9x/evn2LlpYWJ06cwNjYWOocoRD9/PPPxMbGcvbsWalTSqRPnz7RsmVLPD096dq1K1u2bCE+Ph5TU1P69etHpUqVpE4scU6fPo2NjQ0DBgzg999/R11dvVAeV6FQMGPGDHbu3ImPjw9WVlaF8rhlmbu7O+Hh4cjlcgCaNGmCgYEBmZmZfP/998yaNYsmTZpIXFk8fH19GT58OG/evCkRF2KFomFvb893332Hp6en1ClFJi8vj9OnTyOXy/Hx8eHDhw9YWVkhk8nE9y2J5OTkMGbMGHx9ffH19aV3795SJwmCUAKIlcKCIBQJhULB9OnT2bx5M0FBQWIgXILExcUxffp06tWrx6BBg1BVVcXX15eEhATWrVtXIgfCAJGRkVSuXBkjIyOpU4RCZmdnx/nz50lOTpY6pcTJysqifPnyeHl5MXDgQEaMGEFiYiLDhw9n8ODBpKenExsby4ULF3j27JnUuSWGsbExJ0+eJCwsDHt7ez5+/PiPHzMvL4/x48ezZ88eQkNDxUC4kJibm6Ovr09qaipv375l5MiRBUPi1NRUPDw8pE4sNiEhIZibm4uBsJIbMGAAISEh5OTkSJ1SZNTU1DAzM2PPnj0kJSVx5MgRKlasyIgRI9DW1mb48OGEhoYq9eegpFFXV2f//v1MmDABa2trDh06JHWSIAilhFgpLAjCV8nPz2fixIl4enoSHByMiYmJ1EllXlJSEgcOHMDDw4MbN27QtWtXnJ2dGTJkSMEt6iXdyJEjSUtLw9fXV+oUoZDl5eVRt25dli5dKg7r+i/y8/M5ePAgK1euJDMzk0ePHqGmpsb+/fvx9PSkXLlypKamkpWVxYkTJ9DR0ZE6ucS4e/cuffr0oXHjxgQGBqKhofFNj5Obm8uIESMIDg4mLCyMrl27FnJp2ZSamoqWlhZv3rxBU1MTgOzsbCpUqABAaGgoXl5eHDx4UMrMYpGfn0+dOnVwc3Nj7NixUucIRejt27doa2sTGRmJqamp1DnFKjMzk6CgIORyOWFhYWhoaDBo0CBkMhk9evRQ6kMkS5K1a9cyZ86cYj2YVRCEkkkcNCcIQqHKy8tj7NixHD16lLCwMLp16yZ1UpmVlZVFQEAAHh4eREREUKdOHZydnXF2dqZp06ZS532V/Px86taty5IlS8TQUEmNHz+e58+fExYWJnVKiXPv3j02bdrE0KFD8ff3x8HBgf3793Pv3j3Gjx9P8+bNadOmDVOmTKFq1aosX75c6uQS5fnz5/Tu3ZsqVaoQHh7+1Qcfffr0CZlMxpkzZzh27JjS72tbHE6cOIGfnx/Pnj2jXr16TJgwgQYNGqChoYFCoeDMmTPExMSwd+9e5s2bx4gRI6ROLnKXLl2ic+fOJCQkULduXalzhCJmampK27Zt2bhxo9Qpknn37h2+vr7I5XJOnjyJrq4uQ4YMQSaT0aFDhyI9KFT4fDDrqFGjmDNnDm5ubuLzLQhllBgKC4JQaP5cSRUSEkJERASdO3eWOqnMUSgUxMTE4O7uzuHDh8nNzcXe3h4XFxdMTExK7QqMuLg42rVrx7Nnz6hfv77UOUIRCA8Pp3///qSmplKtWjWpc0qcxMREdHV1yc7OZtWqVaSlpTFlyhTq169f8P/18uXL6dq1K2ZmZhLXljwpKSlYWlqSmZlJZGQkenp6X/TnPn78iL29PXFxcQUH9Qj/XIsWLZg3bx62trZs27aNa9eu0aFDB/r374+enh6+vr5ERUVhbm6Os7Oz1LnFYvHixQQGBnL16lWpU4RisHHjRjZt2sTjx4/FMA4KtpiQy+XExMSgr6+Po6MjMplMvO4WobCwsIKV2jt37qRcuXJSJwmCUMzEnsKCIBSKnJwcZDIZ4eHhHD9+XAyEi9mzZ89YtmwZzZo1o3v37jx8+JDNmzeTlJSEu7s7ZmZmpXYgDJ/ftLZs2VIMhJWYmZkZlSpVIiQkROqUEklXVxf4vB1CZGQkgwYNokGDBqiqqpKVlcXs2bM5fvz/Y+++o6K61j6OfymC2CuisaFRE3vBioooVQQEFBgVJhpjjMYSjVeNLWrsNxqNmsTYBjQDAhaagGLB3rAXLBcFESkiqCBlmHn/8JUbrkZNFA5lf9ZiLZ1yzm+GYebMc/Z+dmS5WZDr7zI0NOTgwYMYGRlhampKTEzMW+/z9OlTBg4cyNWrV4mKihKFiQ8kJSUFIyMj6tevT7Vq1Zg5cyZLly4lPT2db7/9losXL+Lp6YmXl1e5KQgDBAcHi/UXyhFHR0fu3r3LpUuXpI5SIhgZGTFhwgSOHz9ObGwsn3/+OYGBgbRp04YOHTqwdOlS7t69K3XMMsfW1pbIyEh27drFkCFDPkj/fUEQyp7SW0UQBKFY5OTkMGTIEKKiojh48CCdO3eWOlK58OzZM7Zu3Yq5uTlNmzbFy8sLuVzO3bt3iYyMxNPTkypVqkgd84MICwvD1tZW6hhCEdLT02PQoEGiZ/Rb3LlzhxYtWtCrVy+0tLQ4e/YskyZNQktLi8DAQJo0aSJ1xBKrWrVq7N27l44dO9K7d+83jshMT0/HysqK+/fvExUVxccff1yMScu2unXrYmFhgUKh4M6dO2g0Gpo1a8aSJUuws7Nj5MiR5W7RxMTERM6dOyeKwuWIsbEx7dq1Y8+ePVJHKXGaNm3KjBkzuHjxIlevXsXBwYGNGzdibGxMr169+Pnnn8XCtB9Qjx49OHr0KNHR0VhZWfH48WOpIwmCUMKIorAgCH/p+fPnODk5cebMGQ4dOkS7du2kjlSmqdXqgoJvvXr1+Oabb/jkk084fvw4MTExzJo1q8yNps3IyODYsWPY2NhIHUUoYk5OTuzdu1eMVHmDnJwcrly5wurVqxk/fjyzZ8+mc+fOTJgwgSpVqhAUFERAQAD79u0jISFB6rgljoGBAQEBAdjY2NCvXz8OHz78ym1SUlIwNzfnyZMnREVFiUJ7EXByciIjI4OFCxdy9epVMjMzAfjqq69o3bo18fHxEicsXqGhoRgaGmJiYiJ1FKEYOTo6iqLwW7Ru3ZqFCxdy69Ytzpw5Q8+ePVm2bBkNGjTA0tKSzZs3k56eLnXMUu/TTz/l+PHjpKWl0bdvXx48eCB1JEEQShDRU1gQhNfKzMzE0dGRmzdvcuDAATGSqgjFxMSgUCjYtm0bDx48wMbGBrlcjr29PRUrVpQ6XpHauXMn9h0Z1AAAIABJREFUHh4epKWlFaxIL5RNz549o27duvj6+uLg4CB1nBJr3bp13Llzh6SkJL777juaNm1K5cqVgRdT0A8fPszPP/9Mp06d2LVrF0ZGRhInLnnUajWTJ0/m999/x8/Pr2CE5oMHD7C0tERfX5+IiAjq1KkjcdKyIyUlBT09vYKe4U+fPmXOnDmcPXuWQYMGkZ+fD4BSqUShUNClSxcp4xYrJycnatSowZYtW6SOIhSjc+fOYWJiQlxc3Dv3ORdevH8fOXIEpVKJv78/T58+xdbWFplMxqBBgwo+D4W/Ly0tDXt7exISEggPD6dVq1ZSRxIEoYiJheYEQfhHnj59ip2dHfHx8Rw8eJCmTZtKHanMSUtLw9fXF4VCwalTp2jfvj1yuZxhw4aVqyLPF198wcOHDwkKCpI6ilAMHB0dqVmzJlu3bpU6Sqnz9OlTjh8/zh9//IGenh7Tp08XJ+veQKPRMH/+fBYtWsTWrVvp3bs3AwYMwNDQkNDQUGrUqCF1xDJDo9Egk8lo0qQJo0aNolGjRlSqVAmA06dPExkZSWZmJpmZmdjZ2WFhYSFx4uKTk5ND7dq1USgUuLi4SB1HKEYajYbGjRszY8YMxo8fL3WcUikvL499+/ahVCrZvXs3Go0GBwcHZDIZ1tbW6OnpSR2x1MnKysLNzY0TJ04QGhoq1okRhDJOFIUFQfjb0tPTsbW1JTU1lQMHDojRDR9QXl4e4eHhKBQKAgMDqVGjBsOHD0cul9OhQwep4xW7l1+YZs6cybhx46SOIxQDhULBlClTSEpKEqtgv4FGoylYsV6j0RAXF4e/vz9BQUHY2toyffp04MWIqqdPnxaMzhRetWbNGiZPnkyNGjXo2LEjgYGBZaYfe0kyb948VqxYwdChQ7Gzs6NPnz7Uq1evVC+C+iFEREQwaNAgUlNTqVatmtRxhGI2fvx4bt26RUREhNRRSr2srCxCQkJQKpWEhoZSqVIlXFxckMlkmJmZoaOjI3XEUkOlUjFmzBh27NhBQEAA1tbWUkcSBKGIvEtRWHwjEwShQFpaGlZWVmRlZREVFUX9+vWljlQmXLhwAYVCwR9//EF6ejoODg4FB2EVKlSQOp5krl69yv3790U/4XLE3t6ezz//nKioKPr37y91nBLrZUE4KyuLixcvsmHDBlJSUvjhhx/o3bs38KIgrK2tzezZs+nQoQOjR4+WMnKJNWDAAKpVq8bjx48xNTUVU4+LiI2NDTk5OdStW5f169dz9OhRhg8fTps2bcp1ET44OJi+ffuKgnA55ejoiJ2dHenp6WJ2wnuqVKkSQ4cOZejQoWRkZLB7926USiVWVlYYGhri6uqKTCajW7duBZ+hwuvp6uqyadMmDA0NGTRoEAqFgmHDhkkdSxAEiZTv0/eCIBR4ufhObm4uhw4dEgXh95SUlMTKlSvp0KEDnTp14uTJk8yfP5/ExMSCHpfluSAMEBYWRsuWLWnWrJnUUYRiUqtWLczMzNi5c6fUUUqFPXv28M0332BkZISfn19BQfjPI4ldXFyYO3cuiYmJUkYtkaKjozEzM8PCwgJ/f39WrFjB5MmTUavVUkcrc1q1aoWFhQVTp07ljz/+QKPRMGvWLBQKBXfu3CmXz7lGoyE4OLigp7VQ/vTr149KlSqxd+9eqaOUKdWrV0culxMWFsaDBw+YPXs2586do0ePHnz88cfMmjWLK1euSB2zRNPS0mLp0qUsX74cDw8PVq9eLXUkQRAkItpHCILAw4cPGTBgABUrViQiIoLatWtLHalUys7OJjAwEIVCQXh4OA0aNMDDwwNPT0+xmMNrDBgwgLZt24oD0XJm3bp1LFmyhLi4uHI/tfxtzp8/z+3btxk6dCgA+fn56OjoFIwSzs3NRU9Pj1mzZnH27FnCw8MlTlxyHD9+HFtbWxwcHNiyZQu6urocOnQIBwcHBg8ezKZNm8r9ibkPTaVSFWoLExERwcqVK6latSpbt24td6O0r1+/TuvWrbl58yYtWrSQOo4gEXd3dwB8fHwkTlL2xcXF4evri1Kp5Pz587Rt2xZ3d3dkMpkYgPAG27dv57PPPuPbb79l8eLFYqS1IJQhoqewIAhvdf/+fQYMGEDNmjUJCwsT09v+Jo1Gw8mTJ1EoFPj6+pKbm8uQIUPw9PTE3NxcFL3+wrNnz6hVqxaBgYGifUQ5k5CQQMOGDTl58iTdu3eXOk6pkZeX90oR82URLjY2lt69ezNmzBjmzZsnUcKS48CBAzg4ODBixAjWr19f6H343Llz2NjY0KtXL3x9falYsaKEScumlyct4MVr9NixY5iZmUmcqvitWLGCjRs3EhMTI3UUQUJKpZKxY8eSkpIiFkYrRjExMSiVSpRKJTdv3qRbt27IZDJcXV1p0KCB1PFKnPDwcFxcXHB1dWXDhg1i3QdBKCNEUVgQhDe6d+8e/fv3p0GDBoSEhIied3/DvXv38Pb2xsvLi1u3bmFubo5cLsfFxaVc9098V4GBgbi5uZGWloaBgYHUcYRi1r17d8zNzVm6dKnUUUqFlyOEAWJjY8nKymL9+vXUqFGD4OBgTE1NuXDhAm5ubkyaNEnitNIKDQ3F2dmZcePG8eOPP752xNONGzewtLTk448/Zs+ePeKzr4j8+XVbHpmZmWFiYsKPP/4odRRBQhkZGdStW5fg4GCsrKykjlPuaDQazp8/j1KpxMfHh4SEBPr164dMJsPFxYVatWpJHbHEOH36NAMHDqRnz574+vpSqVIlqSMJgvCeRFFYEIS/dOfOHfr370/z5s3Fauzv6NmzZwQEBKBQKDh48CAtWrTA09MTDw8PmjRpInW8UmXcuHHExsaKPnvl1LJly9i0aRMxMTFimuI7OnXqFMuXL8fIyIiLFy/Ss2dPLCwsqFy5Mr179xYLGQEBAQHIZDJmzJjB/Pnz3/jaiouLw9LSkqpVq7J3717q1q1bjEnLhj/3thYKe/z4MXXr1iUiIkIsqilgZWVFixYtWLdundRRyjW1Ws3x48dRKpX4+fnx+PFjrK2tkclkODo6iu9CvBhhbWVlRaNGjQgMDBRFc0Eo5URRWBCE14qJiaF///60a9eOXbt2iZGab6BWqzl06BAKhYKAgAB0dXVxd3dHLpfTo0cP8YX4H9BoNDRr1ozJkyeX+1GN5dXNmzdp1aoVV65coU2bNlLHKRVWr15NXFwcjo6ONG3alMaNG//lbcvjCE1vb29GjhzJokWLmD59+jvdJzk5GRsbG7Kysti3bx+NGjUq4pRlw+uKwaJAXJiPjw9ffvmlaBkgAP/tpR8fHy/+TkoIlUpFZGQkSqWSXbt2kZeXh729PTKZDFtbW/T19aWOKJmEhARsbGzQaDSEhYXRsGFDqSMJgvAPiaKwIAivuHLlChYWFnTt2hU/Pz/RT/Ev3Lx5E4VCgbe3Nw8ePMDa2hq5XI6Dg4N4zt5TTEwMn3zyCTExMbRs2VLqOIJEXi4AM3v2bKmjlGpnzpzh7t273L17F5VKxY0bN6hXrx6urq6YmJhIHa9Y/Pbbb4wbN46ffvqJCRMm/K37ZmRk4ODgQGxsLPv27ROLgr6Fl5cXV65c4f79+7i4uNCgQQO6detW7k5CvM2IESPIzc1lx44dUkcRSoD4+HgaN27MmTNnys37cmmSnZ1NaGgoSqWS4OBg9PX1cXZ2RiaTYW5uXi776z5+/BgHBwfu3btHeHg4n376qdSRBEH4B96lKCxWQBKEcuTChQuYm5tjampKQECAKG7+j8ePH/Prr7/Ss2dPWrVqRXBwMJMnT+b+/fuEhITg6uoqnrMPYO/evRgbG4vV2Ms5Jycndu3aJXWMUm3MmDHY2tryzTffMH36dC5cuMAnn3xCw4YNiY2NlTpesVi1ahXjxo3j999//9sFYYDq1asTFhZGhw4d6NOnD9HR0UWQsmy4desWY8aMQS6XY2Fhwe3btwkNDeXnn38utJjatWvXUKlUEiaVVn5+Pnv37sXOzk7qKEIJ0ahRIzp37syePXukjiK8RsWKFXF2dsbPz4+kpCR+/vlnHj58yMCBA/noo4+YMGECx48fpzwNkqtZsyYRERF06tSJ3r17c+rUKakjCYJQRMRIYUEoJ86cOYO1tTU2NjZ4eXmVy7Per5OXl0d4eDgKhYLAwECqV6/O8OHDkcvldOzYUep4ZZKNjQ3NmzcXvfXKuejoaLp06UJsbCxNmzaVOk6pM2vWLI4ePcr69et59OgR58+f5/Tp02zfvl3qaMVCo9Hwww8/sGDBAry9vXF3d3+v7eXl5TFq1Cj27NlDcHAwffv2/UBJy44TJ06wcOFCQkNDAUhLS+PMmTOcPXsWtVrN1KlTOX/+PNu3b2f9+vUSp5XOsWPH6NOnDw8fPsTQ0FDqOEIJsWDBAvz9/bl06ZLUUYR3lJqaSkBAAEqlkqioKBo3boy7uzvu7u506NChXLQCUalUjB07FqVSib+/P7a2tlJHEgThbxAjhQVBAOD48eNYWFjg4OCAt7e3KAgDFy9eZMqUKTRs2BAXFxcA/P39SUhIYNWqVaIgXESysrI4dOgQNjY2UkcRJNapUyeaNGnC7t27pY5SKj158oQ5c+bQpk0b+vbtS8eOHUlPT+fZs2eo1Wqp4xUpjUbDzJkz+eGHH/D393/vgjBAhQoVUCgUfPbZZ1hbWxMcHPwBkpYtzZo14/r168yYMYPHjx9Tq1YtrK2tGTx4MOfOnWPq1KmYmpoyZ84cqaNKKjg4mO7du4uCsFDI4MGDuXz5crmZxVEW1KlThy+//JJDhw4RFxfHxIkTiYyMpFOnTrRu3ZoFCxZw69YtqWMWKV1dXX7//XcmT56Mg4MD27ZtkzqSIAgfmCgKC0IZd/jwYaysrHBzc2Pz5s3luu9fUlISK1eupGPHjnTs2JHjx4/z/fffk5iYiJ+fH/b29lSoUEHqmGXa4cOH0Wg0mJubSx1FkJiWlhZOTk7s3LlT6iilTn5+PpcuXeLp06cAPH/+HIVCQWJiIjo6Omhrl93DO7VazcSJE1mzZg1BQUE4Ojp+sG1ra2uzevVqpk+fjpOTU7kZdf2u6tWrx7Zt20hOTmbTpk2cP38egDZt2jBlyhQePHhAdnY29erVkziptEJCQkTrCOEV7dq1o2nTpqKFRCnVsGFDpkyZwpkzZ7h58ybDhg3Dx8eHli1bYmJiwo8//sj9+/eljlkktLS0WLRoET/++CNyuZyVK1dKHUkQhA9ItI8QhDJs//79ODg4MHr0aFavXl0upjn9r+zsbIKCglAoFISFhVG/fn08PDzw9PTkk08+kTpeuTNx4kSuXbvG/v37pY4ilABRUVH069dPTLP+B4KDgxk1ahTu7u5cunQJY2NjPvvsM8zMzKSOVmTy8/MZM2YMfn5+hISE0KdPnyLb1+rVq5kyZQqrV6/m66+/LrL9lEbR0dFs2bKFihUrYmBggLGxMfHx8QQGBnL8+HH09PSkjiiZe/fu0bRpU86fPy9mHAmvmDx5MhcvXuTgwYNSRxE+AI1Gw6VLl/Dx8UGpVBIXF0efPn2QyWQMGTKEOnXqSB3xg/Px8cHT05PJkyezbNmycvndUhBKk3dpHyGKwoJQRoWGhuLs7MyECRNYvnx5ufrQ1mg0nDp1CoVCgY+PD7m5ubi4uCCXy+nXr1+5Hi0ttZYtW/Lll18ydepUqaMIJUB+fj7169dn8eLFjB49Wuo4pU54eDhnzpyhefPmtG3bltatW5fZ97e8vDw8PT0JCwsjPDycbt26Ffk+vby8GDVqFHPnzmXOnDnl6nP0bXJycoiMjOTq1ats27YNV1dX+vbtW6SF+tJg/fr1LF68mPj4ePF6EV5x8OBBLCwsSE5Opnbt2lLHET4gjUbDyZMnUSqV7Nixg0ePHmFpaYlMJmPw4MFUrVpV6ogfzL59+3BycmLIkCH8/vvvYpalIJRgoigsCOXUnj17GDp0KP/6179YuHBhufliEhcXh7e3N15eXty8eZN+/fohl8txcXEpUwdjpdWdO3f4+OOPuXLlCm3atJE6jlBCfPHFFyQkJBQsXiW8H41GU+be87Ozs3Fzc+PEiRPs27ePDh06FNu+AwMDcXV1ZezYsaxcubJMt+YQ3t/AgQNp1KgRv/32m9RRhBJIpVJhaGjITz/9hKenp9RxhCKiUqk4dOgQSqWSgIAAcnJysLOzQyaTYWdnR8WKFaWO+N7Onj2Lra0t3bp1Y8eOHVSuXFnqSIIgvIZYaE4QyqEdO3YwZMgQ5s6dyw8//FDmigP/69mzZ3h5eTFgwACaNm3K1q1b8fDwIDY2loMHD/LZZ5+JgnAJERYWRqNGjWjdurXUUYQSxNnZmcjISJ48eSJ1FKEEysrKwsHBgbNnzxIVFVWsBWEABwcHwsLC2Lx5MyNHjkSlUhXr/ksiMVDk9TIzMzlw4ACDBg2SOopQQunq6mJnZyf6Cpdxurq6WFhYsGnTJpKSkvDx8UFXVxcPDw8MDQ2Ry+WEhYWRl5cnddR/zMTEhGPHjnHt2jUsLCx49OiR1JEEQfiHRFFYEMqQbdu2MWzYMBYvXszs2bOljlNk1Go1Bw4cQC6XY2RkxMSJE/n44485evQoN2/eZPbs2TRt2lTqmML/2Lt3LzY2NmX+RIXw9/Tv3x99fX0xUvgDyM3NJSUlReoYH8yTJ0+wsbHh1q1bHDlyRLI+8P369ePgwYOEhobi4uJCdna2JDlKiuTkZPLz86WOUeIcOHAALS0tBgwYIHUUoQRzdHQkPDy83L+PlBf6+vo4Ojri4+NDcnIyv/76K2lpaTg4ONCgQQPGjRvHkSNHUKvVUkf921q2bMmxY8fIysqiT58+xMfHSx1JEIR/QBSFBaGM2Lx5c8GKsNOmTZM6TpF4WfA1NjbG0tKS1NRUNm3aRGJiIr/99hu9evUSBccSKjs7m4MHD2Jrayt1FKGE0dfXx87Ojp07d0odpdRLS0ujWbNmZWIUWlpaGhYWFiQlJXHkyBGaNWsmaZ4uXbpw5MgRoqOjsbW1Lbcj29PT02nfvj1BQUFSRylxgoODMTc3p1KlSlJHEUowa2trVCoVkZGRUkcRilmVKlUYNmwYQUFBPHz4kCVLlhATE4OZmRlNmzZl2rRpREdHl6rZGA0aNODw4cPUrVuXXr16ce3aNakjCYLwN4misCCUAb/88gtffPEF69evZ+LEiVLH+aAeP37Mr7/+Sq9evWjVqhWBgYFMnDiRhIQEQkJCcHNzw8DAQOqYwlscOXKE3NxcMYJKeC1nZ2dCQ0PFyKn39HLmxJgxY0r1iOHk5GTMzc3Jzs4mKiqKhg0bSh0JgE8++YRjx47x4MED+vfvX6qf439qzpw5VK1aFRsbG6mjlCgajYaQkBDROkJ4q6pVqzJgwIAycfJO+Odq1arF6NGjiYyMJCEhgalTp3LkyBG6dOnCJ598wvfff8+NGzekjvlOatSoQXh4OF27dqV3796cOHFC6kiCIPwNYqE5QSjlfvrpJ6ZOncrGjRsZOXKk1HE+CJVKRXh4OAqFgsDAQKpVq8awYcOQy+V07NhRjAYuhaZOncq5c+c4dOiQ1FGEEujZs2fUqVMHPz8/7O3tpY5TquXk5NCtWzeaN29OQEBAqXu/TEhIYMCAAVStWpXw8HBq1aoldaRXJCcnY2Njw/Pnz4mIiKBRo0ZSRyoW58+fx8TEhODgYDHr439cuHCBTp06cffuXZo0aSJ1HKGE27BhA3PnzuXBgwdi8UqhkP/85z/4+PigVCq5cuUKHTt2RCaT4e7uTuPGjaWO90b5+fmMGzcOb29v/Pz8sLOzkzqSIJR7YqE5QSjjli1bxrfffou3t3eZKAhfunSJqVOn0rBhQ5ydnVGr1fj5+ZGQkMBPP/1Ep06dSl2BQ3jhZT9hQXidKlWqYGlpya5du6SOUurp6+vj7e1NcHAw27ZtkzrO3xIbG0ufPn2oW7cu+/fvL5EFYQBDQ0MOHjyIoaEhpqam3Lx5U+pIRU6tVjN+/HgcHR1FQfg1goODadu2rSgIC+/E3t6epKQkTp06JXUUoYRp1qwZ3333HZcvX+by5csMHDiQ3377jSZNmtC7d2/WrVtHcnKy1DFfS0dHh19//ZVvv/0WR0dHFAqF1JEEQXgHoigsCKWQRqNh/vz5zJ49Gx8fH4YNGyZ1pH8sKSmJVatW0bFjRzp06MDRo0eZO3cuiYmJ+Pv7Y29vT4UKFaSOKbyHe/fucf36dVFIEN7I2dmZwMBAVCqV1FFKvfbt2zN//nwmTJhQahZ+iYmJoU+fPjRv3pywsDCqV68udaQ3ql69OmFhYXTo0IHevXsTHR0tdaQipVAouHDhAqtWrZI6SokkWkcIf0f9+vXp3r27aCEhvFHbtm1ZtGgRt2/f5tSpU3Tt2pVFixbRoEEDrK2t2bp1KxkZGVLHLERLS4sFCxawevVqRo0axYoVK6SOJAjCW4j2EYJQymg0GmbNmsWPP/6In58fDg4OUkf623JycggKCkKhULB3717q16+Ph4cHnp6ekq0uLxSdrVu38t1335GQkCBGegt/KTU1lXr16rF//37Mzc2ljlPqqVQq+vbtS5UqVQgPDy/Rf3uXLl3C0tKS7t27s2PHDipWrCh1pHeWl5fHyJEjCQoKIigoiL59+0od6YNLS0ujVatWfPPNN3z33XdSxylxkpOTMTIy4siRI5iamkodRygllixZgpeXF9evX5c6ilCK5OfnExUVhVKpxN/fn8zMTAYOHIhMJmPQoEElaqHLHTt2MGLECCZOnMjy5ctFqxRBkMC7tI8QRWFBKEU0Gg3ffvst69evZ9euXaVqOr5Go+HUqVMoFAp8fX3JycnB2dkZuVyOubk5Ojo6UkcUioharSYuLo6mTZtKHUUo4fr370/btm1Zs2aN1FHKhFu3btGxY0dWrFjBuHHjpI7zWmfOnMHa2horKyu8vb1L5cwQtVrNpEmT2LhxI/7+/mWuj+K4cePYv38/ly9fRl9fX+o4JY5CoWDKlCkkJyeLYxnhnSUkJBAdHY2dnZ0olgn/SG5uLhERESiVSvbs2YOWlhaOjo7IZDKsrKxKxOdpZGQkgwcPZvDgwWzevLlEZBKE8kQUhQWhDFGr1UyYMIGtW7cSGBjIgAEDpI70TuLj4/H29sbLy4uYmBj69euHp6cnQ4YMoWrVqlLHEwShBPn5559Zvnw5cXFxJXpka2mybt06pk2bxsWLF2nRooXUcQo5evQoAwcOxMXFhY0bN5bqgppGo+H7779n8eLFbN26leHDh0sd6YM4d+4cXbt2JSwsDCsrK6njlEiurq7o6emVuh7egrQ0Gg1qtbpUv+8JJUdmZibBwcEolUr27t1LlSpVGDJkCDKZjD59+kj6OouOjsbW1pbOnTvj7+9P5cqVJcsiCOWNKAoLQhmhVqv58ssv8fHxITQ0lD59+kgd6Y0yMzMJCAjAy8uLAwcO0KxZM+RyOR4eHmK0qCAIfyk+Pp7GjRtz+vRpunbtKnWcMkGtVmNtbU1mZiZHjhwpMQWIffv24ejoyKhRo1izZk2ZGSm3evVqpkyZwurVq/n666+ljvNe1Go1PXv2pHHjxvj5+Ukdp0TKzc2lbt26/Pbbb7i7u0sdRxAEgfT0dHbu3ImPjw+RkZEYGRnh5uaGTCbDxMREkpPut2/fxtramjp16hASEkKdOnWKPYMglEeiKCwIZYBKpWLUqFEEBgYSFhZGjx49pI70Wmq1msOHD6NQKPD390dHRwc3Nzfkcjm9evUSo/4EQXgn3bp1w8LCgsWLF0sdpcyIj4+nXbt2zJgxgxkzZkgdh6CgIIYMGcKkSZNYtmxZmft88PLyYtSoUcybN4/Zs2eX2se3ceNGJk2axI0bN2jUqJHUcUqkAwcOYGVlRUpKCjVr1pQ6jiAIQiFJSUn4+fmhVCo5fvw4zZs3x93dHZlMRps2bYo1y8OHD7GxsSE7O5vw8HCaNGlSrPsXhPJIFIUFoZTLy8vDw8ODiIgI9u3bR5cuXaSO9Ipbt27h5eWFt7c38fHxWFtbI5fLcXBwwMDAQOp4ggRefl6U1kKIIK0lS5agUCi4ceOG1FHKFG9vbz7//HPOnj1L+/btJcvh6+vLiBEjmDNnDnPmzCmz7xOBgYG4uroyduxYVq5cWepGQj969IhWrVoxbdo0pk+fLnWcEmvq1KmcPXuWw4cPSx1FKCPy8vJE31WhSNy9exdfX1+USiUXL16kXbt2yGQy3N3dMTY2LpYMGRkZDB48mFu3bhEWFkbbtm2LZb+CUF6JorAglGK5ubm4u7tz5MgR9u/fT4cOHaSOVCA9PR1fX18UCgUnTpygbdu2yOVyhg8fTv369aWOJ0hAo9G8/NAps0UeoXjcuHGDTz/9lGvXrvHpp59KHafM0Gg0uLi4cOfOHU6fPi3JgmFbtmxh9OjRLF++nKlTpxb7/ovboUOHcHBwwMnJiU2bNqGrqyt1pHf25ZdfcvjwYS5duoSenp7UcUqsVq1aMXr0aKZNmyZ1FKEUS05O5vjx45w8eZJnz55x8+ZNJk+ejK2trTimEorE9evXUSqVKJVKbt++TY8ePZDJZLi6umJkZFSk+87OzmbEiBFERkYSFBRE7969i3R/glCeiaKwIJRS2dnZDBkyhHPnzhEZGUnr1q2ljoRKpSIiIgKFQsGePXuoWrUqw4cPx9PTk06dOomDVoHTp08TFBREbm4urq6uJXJku1A6tG7dmhEjRvDdd99JHaVMSUlJoU2bNowePbrY23OsW7eOiRMnsm7dOsaOHVus+5bS2bNnsbGxwdTUFF9fXypWrCh1pLc6ffo0PXr0YN++faVmUVsp3Lp1i5YtW3L16tUScZwmlE5BQUFERkZiYGBArVq10NbW5unTp4SEhDBp0iRGjBghdUShDNNoNERHR6NUKvHx8SExMRFzc3NkMhnOzs5F1hYnPz/Q/wpWAAAgAElEQVSfr7/+mq1bt7Jjxw7s7e2LZD+CUN6JorAglEJZWVkMHjyYa9euceDAAVq2bClpnkuXLqFQKNi+fTtpaWnY29sjl8uxtbUV09uEAseOHcPNzQ0zMzMMDQ159OgR8+bNo3nz5qjVarS1tcnPzy8xi1wJJdusWbMIDw/n7NmzUkcpc3bv3o2LiwtHjx6lZ8+exbLPFStWMHPmTDZv3oynp2ex7LMkuX79OlZWVrRo0YLdu3dTrVo1qSP9pfz8fHr06EHz5s3x8fGROk6J9tNPP7FmzRru3LkjTowL/8iUKVOIjIxkypQp9OnTh8aNGxfMKFi9ejW3bt1i7dq1EqcUygu1Ws3Ro0dRKpX4+fnx5MkTbG1tkclk2NvbU7ly5Q+6P41Gw4IFC1i4cCEbNmxg1KhRH3T7giC8W1G4dDU4E4Qy7tmzZ9jZ2XHz5k2ioqIkKwgnJyfz008/0alTJzp06MDRo0eZM2cOiYmJBAQE4ODgIArCQiEbN27E1taW7du3M3HiRCpWrIiHhwdpaWloa2tz7tw5hg4dKnVMoZRwdnbm3LlzxMXFSR2lzBk8eDAeHh7I5XIyMzOLdF8ajYZ58+bx3XffoVQqy2VBGODTTz/l2LFjJCQkMGDAAFJTU6WO9Jc2btzI9evX+fHHH6WOUuIFBwczaNAgURAW/pH4+HgSEhL45ZdfkMvlNGvWDF1dXVJTU9mxYwe7du0qV7MqBOlpa2vTt29ffvnlFxITE9mzZw/Vq1fniy++wNDQEJlMRmBgILm5uR9kf1paWsybN4+1a9fyxRdfsHTpUsRAREEofqIoLAglREZGBtbW1sTHx3P48GGaNWtWrPvPycnB398fBwcHPvroI/79739jY2PDtWvXOHXqFOPHj6d27drFmkkoPVq1akWlSpUAMDY2ZsOGDTRq1KigCLR7927q1asnZUShFOncuTONGzdm9+7dUkcpk1avXk12djYzZswosn1oNBqmTZvGsmXL2LVrV7k/KdS4cWOOHDlCfn4+ffr0IT4+XupIr0hJSWHmzJl8//33fPTRR1LHKdGePHnC4cOHsbOzkzqKUEqp1WouXbpEVlYWt27d4vjx4/j7+7Np0yauXbvGzJkzxSJcgmQqVKiAra0tXl5eJCcno1AoCtrD1atXj9GjRxMZGUl+fv5772vs2LH4+voyb948pkyZglqt/gCPQBCEdyXaRwhCCfD48WOsra158uQJkZGRxfZlTKPRcPr0aRQKBT4+PmRnZ+Ps7IxcLqd///5iqr/wzuLi4nB0dGTVqlX07dsXbW1tNBoNlpaWyOVyVq1axfLly7GwsJA6qlBKTJo0iYsXL3Lo0CGpo5RJkZGRWFpaEhER8cH/LtVqNePHj8fLy4vAwEDRl/ZPMjIysLe35969e+zbt0/yFlF/Nnr0aE6cOMGFCxfEbKC3CAgIQC6Xk5qaWir6RAsl04YNG/jpp58wMjLCxMQEPT09PvroI3r06EGnTp2kjicIr8jIyGDPnj0olUr27dtH3bp1cXV1RSaT0b179/eaOXHw4EEcHR2xt7dny5YtYpFTQfgARE9hQSgFUlNTsbS0JC8vj8jIyGIZTRkfH8+2bdtQKBTExMRgZmaGXC7HxcWlRPc6FEomjUbz8gOHR48eUadOnYL+wYcPH2bMmDE8fPiQjIwMqaMKpcjhw4fp378/Dx8+pG7dulLHKZMmTJjA7t27uXz5MjVq1Pgg21SpVIwaNYo9e/YQGhqKqanpB9luWfL8+XOGDh3K6dOnCQ8PLxHFn5MnT9KrVy8OHDhAv379pI5T4o0cOZL09HR27doldRShlMvMzOTp06fExcXRuHFjjIyMpI4kCO8kJSUFf39/lEolR44cwdjYGHd3d2QyGe3atftH2zx//jy2trZ06NCBgIAAqlSp8trbXbqfTvClRM7eS+N28jNy89Xo6WjzsWEVTJrUwr5DA9p9VP19Hp4glAmiKCwIJVxSUhIWFhbo6uqyb98+6tSpU2T7yszMZOfOnSgUCg4cOECzZs3w9PTEw8MDY2PjItuvUL7k5+ejpaWFtvZ/uxPNnDmTO3fusGPHDgmTCaVNfn4+RkZGLF26lM8//1zqOGVSVlYWHTt2pGfPnigUivfeXm5uLsOHD+fAgQNERETQpUuXD5CybMrLy+Ozzz4jODiY4OBg+vTpI1mW/Px8unbtyqeffsr27dsly1FaqNVq6tevz6JFixg9erTUcYRSLCUlheXLl7NixYpCl7882S4IpUV8fDy+vr4olUqio6Np06ZNQYG4efPmf2tbd+7cwdramlq1ahESElJoYMCZu2nMDbzC9cSnVNDRIi//1RrVy8tb16/GAoc2mDSt9d6PTxBKK1EUFoQS7OWCM9WqVSMsLIxatT78B5ZarSYqKgqFQoG/vz/a2tq4ubnh6emJqampOOAUPoilS5dia2tLu3btChWD//ylRq1WF7pOEN7F559/TlJSEsHBwVJHKbNOnjyJqakp/v7+ODk5/ePt5OTk4OLiwtmzZ9m/f7/ohfkO1Go1kyZNYuPGjfj7+0vWn3bdunXMnDmTGzdu0KBBA0kylCanT5+me/fuJCQkiOdLeG/r1q3js88+o3LlylJHEYQP4ubNmyiVSpRKJTExMXTt2hWZTIabm9s7v2cmJSVha2tLZmYm4eHhNGzUmCVhN9h0NBZtLVC/Q2nq5e0+723MTJtP0NUR30OE8kcUhQWhhIqLi6N///7Uq1eP0NBQqlf/sNNbbt++jUKhwNvbm/j4eKysrJDL5Tg6OmJgYPBB9yWUb8+ePWPIkCHcvn2bxo0b07JlS8zMzDA3NxdTIIX3FhISgrOzM6mpqVStWlXqOGXWrFmz+P3337ly5QqGhoZ/+/4ajYb09HQGDhyIQqEoUX1ySzqNRsP333/P4sWLUSgUDBs2rFj3n5ycTMuWLZk3bx7ffPNNse67tJo7dy6hoaGcPXtW6ihCGZCbmyt6pwqSy8vLe69e8s+fP+fZs2eFRvVqNBouXLiAUqnEx8eH+/fvY2Zmhkwmw8XF5a0LmD958gQnJyeu34ihz0wF5xJzUL1LNfh/6Gpr0f8TQ9YP61yoMJyXl4dGo3nr39+jR4+YP38+CxYsKDh5o6Oj89rBLvn5+ahUKvT09MTgK6FEEEVhQSiBYmNj6d+/P02aNCEoKOiDFTrS09PZsWMHCoWC48eP06ZNG+RyOcOHDxcjWYQi5evryx9//MHEiRM5ceIEV65cKegD2759e5ycnMSoQeEfyc7Opm7dumzcuBE3Nzep45RZubm5dOvWDWNjY3bu3PnaLzJ/Hvn/uqnNeXl5aGlpoaurWyyZy5otW7awdOlS5s6dy/Dhw4ttvyNHjuTs2bNER0eLxeXeUefOnbG3t2f+/PlSRxHKgaysLPT09MR7q1Bknj59Sps2bVixYgVubm5kZ2djYGDA4cOH6du3L/Ci6PumgUVTp07l4sWL7Nu377XHEGq1mhMnTjBx4kSio6MBMDAwoHLlylSqVOm190lNTeWrr74i6lldHlZtxYNN41GlJRRcX+nTPtSyGIM6OxPdmvXR0n6xQLpGo0aVnoS2ngE6lV+sl6CrrcVnvZoy2651wf2jo6MxMTHh0KFDBY/zdU6fPo2LiwtqtRozMzOUSuWbnk7gRTuNhg0bvva6nJwcPDw8mDJlCj169HjrtgThfbxLUViMoReEYnTz5k369u1LixYtCA0Nfe+CsEqlIjQ0FHd3d4yMjJg1axYmJiacO3eOy5cvM23aNFEQForc0KFDadasGXXq1GH27Nn8+9//ZsWKFdjZ2RETEyNGUwn/WMWKFbGzsxOLORUxPT09vLy8+Pjjj//yNqmpqQX/frmw5J9VqFBBFC3ew8iRI4mJiSnWgvCxY8dQKBSsX79eFITfUUJCAufPn2fQoEFSRxHKoP99X71+/TqjRo1i2rRpHD9+XKJUQln3r3/9i8qVKxe8r+nr6wMULEC7detWmjRpwunTp/9yG9999x3Xrl3jwoULr71eW1sbU1NTBg0ahEwmIzw8HDc3N3Jzc0lJSaFbt26sWrWKGzducPfuXe7evcuQIUNIz9MmuUYbtHR00dLRpfbAyTSZEYxurYYYNDMh71ECiVsmEr9aRtwqN+JWuRH/k4zETePJe3S/YP8qtYZNR2M5ezet4LLq1auj0Who1qzZG5+fbt26cfHiRT7//HM2bdpEdnY2KpWKLVu24OjoiEajoXbt2sTGxpKfn09WVhYfffTRX25PX1+fBw8esGnTpoLLNBoNKpUKlUr1xiyCUBTE0bsgFJNr164xYMAAOnfuTEBAABUrVvzH27p8+TIKhYLt27fz6NEjBg0ahK+vL7a2tmIKmlCsXvYKXrVqVcFlDRs2pGHDhnTs2BFHR0fRJ094L05OTowePZrs7Oz3et8U3qx9+/YsX778taN1Vq1axYYNG2jbti2dO3dm5syZYlpkKadSqRg/fjwjRoyQdJG70iY0NJR69eqJRRSFD+blzIv09PSCItxLn376KatWreK3337js88+4+bNmxKlFMqqjRs38uuvv7Jw4cKC4/WXn+/79u3jyy+/REdHh61bt9K1a1fgxQKJhoaGVK1a9ZUWCubm5gX/VqvVPH36lOzs7IJCc4UKFdDW1sbS0hJTU1PWrl1LREQESqWSYcOGUaFCBRwdHRk2bBhqtZpDt1LR6gAaDWhpvyhdqTKSUT1+gEGzLuhUrkHjb3e+02PV0oK5gVcJnVj4M+9tbRw1Gg21atViwYIFb92Htrb2KyOqe/bsycWLF1/5jn7lyhX8/PyAF89VTk4OixcvZurUqe/ycAThgxEjhQWhGFy6dIl+/frRo0cPdu7c+Y8KGykpKaxevZrOnTvTvn17oqKimD17NomJiezcuRNHR0dREBaKVX5+fqGDwby8PC5evMjVq1eBFwd+NWvWFK9L4b3Y2tqSm5tLZGSk1FHKvNcVeoOCgpg7dy4rVqyge/funD9/nr1790qQTviQ1q9fT2xsLMuXL5c6SqkSHBzMwIEDxcKpwgejpaXFyZMn6dy5M9OnT+fkyZOFrq9fvz4zZ87k3r17xMXFSZRSKIuWL1/Ov/71r0It3uLi4li2bBnwYl2HJUuWcPToUQYOHFhwjFCtWjXgRVuF9PR00tPTmTVrFtevXy/4f3p6esHo9j9/D3g5oyghIYHq1atjZGSEXC7H398ffX19VCoV27dvx97eHr/AvaQ8zf3vonL/v//nd86iV78F2gZV0ahyXxlhDy9aSKjzstFo1ACoszNR5Tzn6oMMLidkAC++twCFirgqlYonT56QkpICwOPHj2nfvj2//PJLwX7q1atHjRo1GDduHCEhIdSoUYO0tDTat29PjRo1XunPX6lSJZYsWVLoudm+fTsmJiYF/3/y5Ak5OTmiICxIQowUFoS/4dL9dIIvJXL2Xhq3k5+Rm69GT0ebjw2rYNKkFvYdGtDuo8JnG8+dO4eVlRUWFhZs27btb03RzMnJITg4GIVCwd69ezE0NMTDw4Nt27bRunXrt29AEIqQk5MT/fv3Z/LkyVy/fp358+fz8OFDatasiYmJCV9//fUHX0RRKH+qVauGpaUlu3btws7OTuo45U5qaiqff/45gwYNwtTUlMuXL3P58mVsbW2ljib8Q0lJScyZM4eFCxeKBUH/huzsbPbv34+3t7fUUYQypkePHqSnp3P9+nVOnTpFdnY2bm5uGBkZ0atXL5RKJdbW1qLNi/DBJCcn4+fnx65duwpm+507dw47Ozs8PT0BWLNmTaGC8enTp+natSsajYbhw4cXHOMfPXqUefPm4ejoSP369QtuX7lyZczMzMjNzS0YKfxSw4YNC1olXLlyhUGDBnH37t2C6x89eoSJlTOP/7+o+2dZt09h0NyEnIQbJG2f/sbH2eDLDVSo2YBHe1eTFfOiSN1+WeHbvO7vqnbt2qSmpqKvr0+7du0YN24coaGhBAUFkZWVxeXLl9m7dy/Hjh1j27Zt1KlTh7Nnz7J169ZC7bbgRbuI/z3pnpWVxe3btwtdlpeXh1qtfuW5EoSiJorCgvAOztxNY27gFa4nPqWCjhZ5+f89I5mdpyY6Lp3LCRlsOPIfWtevxgKHNpg0rcXJkyexsbHBwcGBzZs3v1O/RY1Gw5kzZ1AoFPj4+PD8+XOcnZ0JCQlhwIAB6OjoFOVDFYR3NnnyZJYtW4aJiQkbNmygdu3auLu7Y2BgwJIlS9BoNMyYMUP0GRXem5OTEzNmzODXX38Vr6diVqNGDfLy8sjPz6dmzZrY29uzdu1aJk+ejLa29lt/H0+ePCkYVQQvFrVLTU1FV1cXQ0PDgsvv37/Po0ePCqZSt2jRgvz8fC5fvgy8mFoJL0YZvVyY5dSpUwWLWvbs2bPgS1daWhrR0dFUrlyZHj16FFx+48YNjhw5gq6uLgMGDKBx48bAixOwSqWSjIwMHBwcMDY2Lsh16tQpzp8/T/PmzbG0tCQvL4+NGzdy+/btgh6ANWrUoG/fvlhYWAAvPscXLFjAiBEjaN68eaHnY+vWrVSuXJmhQ4cWunzp0qUMHTr0ldtrNBp8fX0BcHd3L3TdxYsX2bx5M6tXry64LCMjA3d39zeO5l6wYAHGxsaMGzfuL28jvOrQoUPk5eVhaWkpdRShDOrWrRsjR47E3t6e0NBQtmzZgra2NitWrCA9PZ1FixYVKrgJwvswNDTkzJkzAAVF4S5duvDgwYOC193t27cLisL37t2jV69ebNu2DWdnZ7y9vQs+WxctWsSkSZNo2bJloX0YGxtz4MABcnJyXpvh5SKKf6ZSqcjJyaF27dqo9SqD1quzMirUbkjuwztU7+lK42m7QFsXLS0tUnYtpkKdJtToMxyNRoNGlYOW7ovt17L8ipoWY9DS0qF9w+psknclJCSE0aNHExYWRocOHQq2n5OTQ35+PvBilO8ff/xBs2bNCtq7vDzuiY+Pf+3f5P/OJNHS0nrlsgoVKnDv3r1XisXjx49n7dq1r32+BKGoiG9WgvAGqnw1S8JusOloLNr//57954Lwn728/MbDJwz57QQ2xhXY/u1Q3FyH8ttvv721mHv//n28vb3x8vLixo0b9O3blxUrVjBkyJBCX6gFoaTo378/ly9fZuzYsRgZGbFixQo6deoEQNOmTXFxcWHKlCmiiCe8NwcHB8aMGcOxY8cwMzOTOk6ZFxERgZWVFfCiIN+jRw90dHQKFlPJyMhAo9Ggq6tLbm7uX7aIyc/P58SJE1hbWwMvVi//+eefiYyMxMDAgC+++AI7OzueP3/OgAEDCr40ValShc2bN5Odnc2YMWMwMDBAS0uLR48ece/ePVQqFbt372bBggVkZ2ej0WiYP38+rq6uJCcns3DhQh4/fkx2djZ9+vRh0qRJpKWlMW/ePDQaDZmZmURERLBlyxb09fX597//TWhoKC1btuTy5cusW7cOfX19Dh06xPr162nUqBERERGkp6cXFHMrVKiAnp4e+fn5LF++nPbt2xc87qCgILy9vfnqq68KPR+3bt1i9uzZryycOG/ePFauXIm5ufkrReH79+8zbtw4pk2bVujyvLw8Ro0aRc2aNQsuS09PZ9CgQZw7d+6Nv9+AgAACAgLEe/PfFBwcjJmZ2XsvEiwIryOTyViyZAlOTk44ODjg4ODAs2fPSElJKXSiShA+tNzcXPLy8graI+Tn52NjY4Ozs3Oh1gydO3emb9++dOnShdjYWHR0dMjNzSUnJ4fjx4+zfv36F8VYjYZnz54V9CjOycnhwoULqNVqEhMTSUtL49y5cwwePJjU1FS0tbV5/vw5VatWRaVSYWBggL+/PxlZufCaCYc1TGXE/+xBXvJd9Iyak5sci57hf/9G1HnZ5D99RIVa/13sTafKfz8r7+dUwMjIiAsXLqCrq0tISEjBccpf+eGHHwpOTmtra6PRaAgKCmLevHnAi2OC+Ph4NBrNKwXgvLy8137eNm/evNBoYbVaLRaaEyQhjgaFMi8/P5/09HRq1679t+6nylcz7o9oDtxIBvhvP6O3eHm7vbef03rMStYvkf9lQTgzM5Ndu3ahUCiIjIzE2NgYmUzG0KFDC52xFISS6mWxZeHChaxdu5YffviB+vXrk5qaSk5ODpUqVZI6olAG1K1blz59+rBr1y5RFC5iCQkJTJgwgYULF+Lq6gpQ0GJAS0uLLl26oK+vj76+Pv/5z3/w9PTkl19+oV27dq9sS0tLq9CiM/v27SMkJITdu3dz4sQJ3NzcePr0KTk5OajVamJiYl7ZxpUrVwr+/fvvv5Oamkp+fj4bN25k2bJlWFpasmnTJr777jtcXV05ePAgSUlJKJVKzp8/T69evZgwYQKnT5+mQoUKbNu2DQATExOOHj1KmzZtWL58OdHR0TRv3pyvvvqKsLAwBg0axM6dOxk8eDDDhg1DqVSycOFChg4dWqjY+/jxY86cOYOLiwvwYpTyokWL8PPzw9DQsGAxTo1Gw/fff8+3335bMP1WS0uLsLAw7t69S4sWLV7bp3bSpEl88sknr0xvXbt2LXp6egW9EHNzc1mzZg1DhgwhOjr6jb9jd3d3TE1N33gboTCNRkNISAiTJ0+WOopQRrm4uLB9+3bUajXZ2dkkJSVRv379QgXhvLy8D9JCQqPRoFarC37+9/9/9SPV7UpDxtL8WFQqFXv37n3rQmrR0dF89NFHr73uyZMnr1yWmZlZ8O8/t6EAXjub5dmzZ8CLVj0WFhYYtDbjda927YpVqNjwU57/5yxqVQ5J26bT4MsNBdc/jtxIXspdjDz+/dqsOar8gr6+GzduZMKECYwfP55WrVq99vYADx8+RFtbG0NDQ7S0tNixYwdPnjzB0dERgOHDh+Po6EhWVhZjx44tdN+nT5++U0sIbW1tsQ6LIAlRFBbKvHXr1jF37lxu375NnTp1Xrn+119/5fvvvy90Bk+j0ZD+7Dn1v96G6v+rvA82jqOm+SgMmpu8dZ/ZcVdI+mMG2pP+YHnETWbb/bf/r1qtJioqCi8vL/z8/NDW1sbV1ZWoqChMTU1xcnLi/v37bN269f0fvCAUoZfFjvnz52NoaMiePXv45ZdfePDgAQkJCcyaNUvqiEIZ4uTkxMqVK1m1atVrF0QTPoxHjx6RkpLC2rVref78OXK5vNDznZeXR8WKFfH19WXWrFl88cUXry0I/y+NRkPFihXZtm0bNWvWpHfv3lSpUoWsrCxiY2OpVKkSV69eJScnh6ZNm1KrVi3gvwtaJiYmsnz5ci5evAjAnDlz6N69O/n5+bRu3bogY5MmTdi0aRM6Ojo0aNAAIyMjcnJyePLkScFJqszMTAwMDKhQoQLXrl3D2NiY5s2bk5eXx/Dhw/njjz9wdHTE3d2dXr16AS/e75o0aQK8mN6qo6NDXl4ekydPRiaTFez/559/5saNG5w8eZLnz58X3D8kJIQdO3bQvHlzduzYgaurK2q1mlatWjF79mzGjx9fMArppV27dpGRkYGHhwdpaWkFl9++fZtNmzbxyy+/MH/+fODFdNbRo0dTuXLlV0YV/6/Zs2e/9fclFHbt2jXu3r3LoEGDpI7yzl6O2CtLxavS/lj09fUJCgp67ajBKlWqEBERAbyYjr9kyRImTpzI48eP+fLLL2nUqBGNGjUqdJ+X3ylevp+8a8aSRFtb+40/L6fev8vPu962KG6no6NTqh7Ly9dGxYoVC2W/dOkSixYtYufOna/cX6VSoa+vj67ui3YN+/fvZ8qUKaxZswZzc3MMDAxQqVQMHDgQT09PRo8e/cp+16xZw40bN/j1118LiqDXr1/H2dmZ27dvs2zZMlq2bEnlypVxn/7jK68Xdd5zsu9fJy/tAQYfd+fZhXAMWnSnQs3/tnGo1tWRBxvH8/w/5zBo1uWVbejr6jB79mzatGmDXC7n8uXLjBw5kqNHj/7lIqKrV6/G19eXmJgYNBoNtra2WFlZFTwvS5cuZenSpSxYsOCVAnliYiINGzZk+vTpryzu+ufjK2dnZwICAv7W348gfAiiKCyUaZcvX2bOnDl069aNsWPH4uPj88qB2NixY185o3fmbhquv50oKAgDaOlUAJ13OzuvpfNiHyotbTYdjcWmjRE1VGl4eXnh7e1NXFwclpaWbNiwgcGDBxda9XTq1KmYmZnh7u6OjY3NP33oglBkcnJy0NfXR1tbG7VaTX5+Pm5ubmRmZhIbG0uXLl2YOnUqH3/8sdRRhTLEycmJyZMnEx0dTZcurx7kC+/n5cjVSpUq4eDggLOzMytXrqR69eoMHjwYoGBaY2xsLP/H3n1GRXF4DRh/aFLsYgPBig1BUDSisRCVCPYGKoJBTYyKNdbYG7bEbiyJDUEsYEVixW5MrKCAiAVBQLpUabs77wde9i+CHViQ+Z3jQZbZmTuwMDt37tw7cuRI1q9fz5gxYwpcX2ZmJmFhYcyZM4fRo0djZWUlb0kREhLCmjVr6NGjB1paWjx48ICkpCSGDRtGZmYmXbt25Y8//kBZWVnetuL48eP07NlTntht166d/DbNP/74Azs7OwB5v+EzZ86wbt06Ro4ciaamJp06dWLu3Lls2bKF8PBwALp06YKnp6e8OkhNTY2KFSsSGRkJQIcOHcjKymLu3LkcP36cQ4cOAf/rJxgbG8utW7fYuXOnfL9WrlxJnz59CAoK4uDBg8yYMYMePXrw66+/0qJFC1RVVdmxYwd37txh1apV8irAt3suxsfHM3fuXA4cOMCtW7fk/Q0FQeDnn39m7ty56OjoyBM8ysrK6OrqyttpvI9MJiMyMrLMJOIKY7nAwEAqVKjAjBkzSmyMBVV/lhSKTtgVViJOVVX1i7cdEhJCo0aNCkw+SSQSVFVVadu2LW3atGHWrFl4eXmxfv16Hj58SJMmTWjfvj1Tp06VJ/I6d+7MtrQ1uQsAACAASURBVG3bkMlkJeL7+CnLiRd4Fef27du0bdv2nV/v2LFjgY8/fvxY/v4+MzOThQsX4uHhwdy5c+natSsAOjo6zJs3r8DXuIaGBmpqavz555/MmzcPdXV1BEEgOzsbTU1NpFKp/NiuZZA/PtnrZKLdZqLZyAzNhq15dWkPtYevzLOMmrY+Ws07kXjNvcCksNqzq+zx3MPt27eBnD77ZmZmODk5sWXLlnyvy8TERLZv386yZcvklfoDBgwosGo/Li4Oe3t7+efp6emEh4fTqFEjrl69yg8//FBg4dfs2bPzDNoTiYqTmBQWfbVCQ0Pp06cP8+fPZ8qUKdja2tK/f3/279+frx9cUlISKSkp8s9n7v0XWYYEJY03lvuUNy//nxRWUlIGBIatOcaTzaNp0aIF48ePZ/jw4ejq6hb41E6dOjFgwAB+/PFHHj9+nCdhLBKVBPv37+f27dts3ryZ1NRU1q5dy59//knfvn0xMTHBxsamwKp8kehL1K1bFzMzM44ePVomk8Lvqvr7mOSQiooKNWrUkJ+IvykhIYFnz57Rpk3OXTAGBgbY2tpiZWVFQkIC69evp0KFCnTv3h1VVVW0tbXp2rUrlpaW8kTs27Kzs3nx4gVxcXF07tyZgQMHEhAQQIMGDZBKpSxdupSTJ0+yZcsWIOdnu3btWgYOHEhGRgatWrXi3Llz8h5/WVlZ7N69m3379uX5figpKXH27FmuX7/Oxo0b5V+TSqVcu3aN+Ph4eU/+7OxsDA0N8ff3RyqV0r59eyIjI/NNBVdTU8uToJXJZCgpKVGvXj38/PwwNTWVP+7l5UW3bt1QVVVFEAQuXrxI7dq1cXZ2pnLlyvI7lZo1a0ZgYCDXr1+nefPmdOjQgX79+jFz5ky0tbXJzMxESUkpTwXf0qVLWbRoES1btuTx48ekp6cD4OLigpmZGcOGDePVq1doaGjk+X58jLZt2xIWFvZRy36Kt5NBpSEp9aHkX+5y8fHxGBgYoKenV2JiLMrlCnOdYuLv4+VecOrfvz/jx48nIyMDKysrKlSoQIMGDTh9+jQaGho8ffpUPtRLWVmZgQMHKjJsUSlkYmJCVFRUvrYG//77L2PGjOH+/ft5HpfJZGRmZuYZEGtmZoaZmRkLFy7kzJkzDB48WH48nDZtGhMnTqRhw4ZAzrm2n58f165d49GjRwQFBaGiokJaWhra2tqYm5tjamqKiYkJLVq0ICwsjDGzl5ElyZZvT5BJUa1cizrjdqJauSbxpzejrtMYdd38bR8qfTOAqD1TSH/ui2b9nOO2IMhI/c+T8Gvu7HVxoVmzZkDOMLmjR49ibm5OSkoKf/31V57z77lz51K9enX5RXCZTIaPjw/169fPt91FixblubPn8uXLaGhoULdu3Q+2fhGHyYsUReljriIrKSkJJelqs0j0IQ8ePKB37978+OOPzJ8/H8g5IRw7diyXL19m06ZNWFtby5efOXMm7u7u1K9fn7QsCQ8ePUOjrjFVOo8gYosjKpVqIk1LQFmjAkJWBpU72FK5fU6vxYzwh0S7zUBJvfz/AhBkCFnpeR7TUIH012n4+/vTokWLAuPOvSX1yZMnxMXF0b59+yL47ohEXyYoKIgBAwbg5OREbGwsYWFh8oq/tWvXoq6uzqFDh6hcuYDpEKL3ejPxJ1bt5f93/fp1AgICGDlyZJnZ59x/n8vc3JyzZ8+iqalZ4C3LmzZtwtvbm9OnT+frVxkfH8+hQ4c4ePAgK1eulFfhfug1fOPGDapVqyY/4erfvz89e/bkp59+kieInj9/jpGREaGhoVStWhVlZWV5q4jc4/ayZcuAnGP6sGHD8vQXBggLC8PKyopFixbJ+x+npaXJh9skJSVRt25d7t+/z86dOylXrpy8dcLs2bMpX748gwYNYvjw4dy7dw+A69evs3btWg4fPkxUVJS8n7Kvry8WFhYkJibK99Pc3Jzt27djamqKIAg4Ozvz8uVL/vjjD+B/09qPHTvG0KFDefr0qTz2qlWr8uDBA/T09JBIJHTv3p3ly5fToUMHwsLCqF+/Ph06dEBFRYWoqCiys7Oxt7fH1dUVmUyGrq4uaWlpPH/+nI4dO/L3338DOQn03Gqrd4mIiCAtLa1QE4Bfc9VfQkICNWrUwMfHBwsLC0WHI/rKvXz5ksTERIYNG0bDhg1p2bIlGRkZ1K9fHwsLC2rVqkXVqlU/6UKQSPSxrly5gr29/QcvHAqCwNOnT7lw4QL79+/nzp07zJ49m+nTp+Ph4cHvv/+On58f+vr6SKVSIiIiUFJSomrVqlSoUIGff/4ZExMTTExM5H2K79y5w969ezlw4ABJSUlUr1OfVJ1WVO3sAOS0cqz0zUAqtOwOQOLVfajrNkWtRj1eB/9Lyp0TVDDuTuUOQwBIC7qOlsE3oKxM+pNbJF7bh+TVS1Zv3Mr0sY759um///7DysqK2rVrs3HjRiwtLbl27RqdO3fG3d2doUOHAlCxYkV5/+OCODk5sXnzZgD69OmDkpISJ06cYPHixTg7Oxc4byUjI4P+/ftz4MCBD/yERKJPo6SkhCAI7z1YiJXCoq+KTCZjy5YtzJo1iylTpjBhwgT5CRzA6tWrWbFiBT179uSbb77h559/ZvDgwairqzN06FB+//13lv/9kNDli8lOTURJVQ2UlNEbv4uXe6ZQ9btRpD7wAeX/XclTUs05gdZz2oNyuf8f+BL9jJe7J8kfU1NRYlBTTVaN6Cqv6oGcW0oOHjyIlZUVqqqq1KhRg1OnTmFlZUXjxo2BnIqn9evXY2dnh47O//oliUq3L+nzV5zLdezYMd+QxmbNmnHo0CHmzp3L06dPmT59uvyNUt++fWnTpg3+/v4FDjKKj4/nzz//LNH7rMhtl6QLsB9T/VXcFWyCIJCQkMDt27epVKnSR6/vzaq/krIvRbHc28tWqFCBOnXqvDdhV6dOHXn/3qCgIJKTk6lZsyaNGzdGW1sbGxsbsrKycHJywsXFJd+wmLcJgoCdnR1Xr14Fco5hGRkZ1K5dG29vb5o0aUKTJk2oW7cu9evXJzw8nCNHjtCxY0eaN29OVlYWN27ckFe+CYLA0aNH5X9jcqWmprJgwQIGDBggTwgDHDp0iAYNGmBhYUGFChXQ1tbm9evXREdHy+9gyMjIkE9Mb9q0KSkpKVy7do1WrVqxdetWeR/gefPm8euvv9KoUSNUVFTyVFTFxMQQFRUlrxxWUlKidevW8knkAMePH8fY2JhGjRqRnJxMQkIC1apV4/bt2ygrK8tPhHNfo7k/I21tbU6dOiWfBn/x4kVevXqFjY0NvXv3JisrC6lUysuXL1m7di3Ozs7ybeau633eNShIVLDTp09TsWJFcTifqFiEhoZy+PBh6tevz5MnT1iyZAn16tVDRUWFf//9l8zMTCpXroyycsG9T0WiL5GZmZmvndHbjh49yrBhw5BIJBgbG9O8eXOGDRvG6dOnWbVqFcnJyZQvX55mzZoRGRmJqakpnp6eGBsb4+zszMaNG/nzz5zBcFKplNTUVFJTU+U9i8uXL0+tWrVIiImgZkMzUMoZ5C5IJXniqNJpOACy7AyS/jmImrYemo3/d/G6fLOcv9nZr14S//cGytWoR/tZu5g+dliB+9WuXTtu376NnZ0dK1eu5LvvvqNjx47s2rWLIUOGyJfLysoiJCTknZXCL1++lC9Xrlw5HB0dgZz3HnZ2du9sH/H48eP3ft9FoqIiJoVFX42YmBi6dOlCfHw88+fPZ8GCBfLbSVNTU+VVi5mZmezcuZPAwECWLFmCpaUlmpqauLq64unpSWxKJpkSGVpN2+dJ/r5JSemNpDAfvkqfLRXwC3sl335qaiqCIHD79m1GjhzJqVOn5D0NU1NTiYiIkCeJMjMz2bNnD15eXnmSaYpOXpWGBFtJWO5dy5Yk70ouValSpcD+VjKZDGNjY4YMGcKsWbM4cOAAgwcPpmLFiiQmJhIWFiZPlLwpKysLLy8vTpw4USQJsY8Z8lEcibjSvFzusiW56q9Zs2byHq2izyeT5fSf7Ny5M87OztjY2JCZmcmtW7do06YNlpaWTJo0ierVq8tPhj6m+l9JSYlevXqxZcsWpk2bxu3bt/H396dDhw5s27aNrVu3snHjRh48eIBEIsHIyIhTp06xcOFCpk6dyr179wgNDZX3MZbJZBw6dIi//vorz3aWLFnC3r17OXnyJMePH0dFRQVra2t5j8OxY8fy/PlzDAwMaN68OT179mT+/PlkZ2eTkpLCxYsX5QPp5syZw7JlyzAwMODFixfyQTDVqlVj6tSpmJqaEhAQkCfhe/jwYXkldK4uXbrQtGlTRo8eTVZWlnxoTrVq1fjll1/kt56+fPmSpUuX5vkdi4qKkreIKF++vLx1BuRcTAsJCck30O/ly5f8/vvvtGrVKs/j2dnZiAqPt7c3PXr0+OCtvyJRYTAyMkJZWZkGDRqwbds2jIyMePjwIdOnT0dVVZXs7GwqVarE7NmzC3yvJRJ9CUtLS6Kjo/M9Hh0dja+vL35+fty7d4+aNWsSERGBr68vcXFxmJqa0rlzZyZOnIipqam8b3Zugjn3ouqCBQuYNGkSp06dYu/evVy6dIlGjRoxZcoU7O3tadSoUZ7t3n6egM32GwDU+fnPAmNWVtNAf5LbO/dJraoOOqM3oVaxOmtGv/8u3EaNGnHjxg1SUlLkF1hzk7q53pc0X7Rokfz/5cqVyzM4buHChe88B1y5cmWBj4tExUFsHyH6qhw6dIju3bvLq54A+Ulh7oCcNwmCQHx8PJ6envLHlp4MJEMiBZTQavYt4RvtqTfrRJ5K4XI161PpmwHA/6qC9X/xyFcp/OZjmTHPido1oQj3/v0UnWwqywm2krrcpyb+CrpV8ejRo0ybNo0qVarw3XffERMTQ8WKFeW9QkWiwvbrr7/i4+PDzZs3FR1KqRQcHCzvRZl7DPzuu++oVasWa9asQVlZmQcPHnDmzBlmzpwpb3eUm0T+GK9fv8bJyYmLFy+ioaHB5s2b6d4953bPyZMn4+3tjZ6eHtu2bZMnVsePH8+pU6do0qQJy5cvl/eNzs7Oplu3bpw5cyZPj79hw4aRlJREamoqyso5F4W8vb3R0NBg3bp1nDlzhjp16rB8+XJq1aoFwPbt2zl37hyqqqr07ds3T0/kAwcO8ODBA2xtbTExMZF/f9avX8+zZ88wMjLi559/li9/+fJlBEHA4q12AlFRUXh4ePD69Wtatmwpb1UllUo5evQosbGx1K5dm379+uX5fl69epUWLVrI37/IZLLcW/6IiooiMzOTBg0a5LmbIDMzk+fPn+dLTgcGBmJoaPhRPyvR+0kkEmrWrMmGDRtwcHBQdDiiMsrGxgYlJSU2bdpERkYGe/fuxdfXN0/CSSQqDBKJhODgYHkCOPdjdHQ0ampqGBoaYmJiIu//a2Jiku+Ownet99y5c+zdu5djx46hqanJkCFDGDFiBObm5u88H0lMTOTbCWtJq9PmncVaH0NVWQnHDvWZ10s8NorKlo9pHyEmhUVfndTUVNLS0ihXrhxKSkqEhYXRunVr4uLiAOQTTjU0NKhUqRJ+fn506dKFVatWoayszLzDd5EolyPh7Fb0xu0ifLND/p7C3w6lsvlgADKjnhC1Z8oHeworISDLfM2RI0fkg0pWr15NYGAg7u7uZGdnY2ZmhouLC+3bt8+X2HN2dmbfvn34+PjQoEGDT0oCltSKP1Hpc/bsWbp165ZnGEJSUhJubm48evSIQYMG0b59e8qVK6fAKEVfs1u3bvHNN98QFhaGvr6+osMpVeLj4xkwYAA7duzAwMBAnpScMGEC586d49KlS+jo6BAaGoqtrS3Lly+nW7duCo5aJFKcq1ev0qVLF6Kjo6lRo4aiwxGVEVKpFJlMhpqaGsnJycybN4/27dszbFjObe9Pnz5lxIgRXL9+XcGRikqzpKQk7t+/L0/8+vn54e/vT0ZGBtWqVZMnfnM/Nm/e/JPe3wuCgK+vL3v37mX//v0kJCTQq1cvHBwc6NWrV74hd2+LjY2lR48epGdm0WbyVm6EpiCRfXpOSlVZiW7NavKHXWtUVcS2K6KyRewpLCqT9u3bx+TJk+UTxWUyGVKpVN73RxAEsrKymD17NgsXLqRcuXJoaGgQExODnp4emS8C0DCxRklVDUEmzddTWJqagHL5Kv/boCxnmMv7egrD/yqFly9fzjfffIOhoSHXr1+nZ8+eGBsbk5GRAeRMYc/tJ/ymVatWIZVKadiwoXhiIlKIuLg47OzseP36NQMGDKBNmzb069ePhg0bEhMTI2/XIhIVpTZt2qCnp8exY8eYOHGiosMpVSQSCS9fvkRDQwNlZWWSk5MpV64cnTt3JjQ0lEePHqGjo0O9evXQ0NDIM0FbJCqLvL29MTc3F993iYpVTEwMlpaWdO7cGX19fS5evEhkZCQNGzYkLCyM5cuXM3z4cHHYnOijCILA8+fP5Ynf3CRwSEgISkpKGBgYYGpqSv/+/Vm4cCGmpqbyeQSfIzw8nH379uHq6kpAQADm5uYsWLCAIUOGfFRVMeQMRO3evTtaWlpcvXyJKlWrseJ0EDuvhaD8/z2GP0RZCQQBHDvUZ7ZVMzEhLBK9g1gpLPoqXb16ldDQUOzt7fO1j1i3bh06OjryoTVBQUFYWFhw/fp1oqOj6TdyIpq9fyX8D0f0JriQ8ewuWk07yJPCajUbgCBDRSunt6IsK53s+HDK1W6EklLOwaag9hGNK0Pn7LukpKTIb8sJDQ0FoHr16jRv3pyrV68yadIkBgwYgKGhITVr1iQ7O1t+a6xIpGj+/v4MHjyYpk2bUqdOHS5fvkxaWhphYWHcvHkTMzMz8QRFVOQmTZqEv78/Fy5cUHQoJV5u0iD346RJk6hSpQoRERGEhISQlJTEhAkTOH36NFpaWqirqxMTE0N4eLjYouMrljsAR/R+RkZG2NnZMWfOHEWHIipjLly4QGRkJAcOHEBVVZXr169TqVIlWrdujZGREbNmzcozvFokgpxhZv7+/nkSwPfv3ycpKQktLS1atmyZp/rX2NiYChUqfPF2U1JSOHLkCK6urly4cIH69etjb2+Pg4NDgcVO7/Ps2TO6d++Orq4u3t7eeeYZ3HqewK+H7/EkLgNVJZAUkKJSU1EiWypgqFOJJX1b0KZ+tfwLiURlhFgpLCqzlJWVGTduHE2aNKFmzZryx+Pi4li8eDEuLi75nrN79242btyIikYFMoKu5KxHTQOtph3yLBfvvQ61GvWo2uWHnGXKaaKu8/6DnSDJJuDKWRrXB3t7e9q3b4+SkhLPnj0jOjqaZ8+ecf/+fa5evYqnpyebN29GJpNRvXp19PX1CQkJYcaMGXTo0EGeLBaJiptUKsXIyAg3Nzc2b97MtGnT2LJlC7a2ttSvX5+RI0fSt29fnJ2dFR2q6Cs3YMAAtmzZQlxcHNWrV1d0OCXay5cv0dXVRSKRoKamhpqaGuvWrWPmzJk4ODjg4+PDf//9x5gxY1BSUiI4OBipVIqTk5OiQxcVgbi4OPbs2UNsbCxOTk7UrVtX0SGVWM+fPycgIIBevXopOhRRGSOTyejatSuQc94AOdXDWlpaJCYmUq1aNVRUVMjMzPzgLfiir1d0dHSeyl9fX18ePXqEVCpFT08PExMTOnXqxMSJEzExMaFRo0aFWmQklUrx8fFh7969HD16FDU1NWxtbVm0aBHffvvtZxWJBAYGYmlpiZGREUeOHKF8+fJ5vt62fjUGaj7C+aQrY1bt5E5YIk9iUsmUSFFXVcGgZgXa1KtK75a6GNf58HBckUgkVgqLvjLp6emkpKSgrq7OsmXLSE1NZeLEibRs2ZLExEQmT55MdnY2GzZsQCKRoKSkRExMDF27diUqKor+/ftj0duGtQ/VifjDEf2pB+XrfrlnCuWbdyYj7AFZ0U+p47RHXhn8tsyXwUS5/IL+1EMoq2sB8KN+PBc893Dp0iUaNGjA8OHDGT58OE2bNgVyqnbU1dVZsmQJffr0ISQkhCdPnuDh4YGvry/16tXj2bNn8mSxoaEhLVq0oEWLFvL/i8liUVHKrTTMzs5my5Yt3L17FxcXF9q0acOhQ4do2LChWH0mKhYSiYTatWvz22+/MXLkSEWHUyIJgoCfnx+tW7cmMDBQPozsv//+w9ramrVr18onav/www+8fv0aDw8PBUYs+hIymYysrCzU1NTynPRLpTktrlRUVPD19eWnn37i+fPn9OnTh6lTp2JsbKyokEu8P/74g5UrVxIWFibeASMqdrnvuWQyGeHh4dSpUwepVIqnpydeXl7cvXuXgQMHMm7cOPHizlcud/jbmwlgPz8/oqKiUFVVxdDQMF//349t0/A5/Pz8cHV1xd3dndjYWKytrRkxYgS9e/f+our1u3fv0qNHDzp16sT+/fvfecHj+++/p1GjRmzduvWztyUSlRXioDlRmXPw4EHs7e1RV1dHVfXdhfC5w+aMjY3Zvn07VlZWREVF4ezsTLt27Rizcjex0VFUbNOP5NvHAVBSLYdqldqoVNDm1YUd1LRZiLpOkwLXnx5yj5iD89GbtA+18pVpVrsSf0/qBMCLFy/Yv38/rq6u+Pv707ZtW+zt7RkyZAiOjo6cPn06z7pUVVVZsmQJv/76K+np6QQHBxMQEEBAQACBgYEEBATw9OnTfMniN5PGNWrUEE9oRIVuxowZXLp0iYiICCIjI5HJZPLBVSJRURs1ahRxcXGcOHFC0aGUSIIgkJGRQZ06dahXrx6nTp2idu3aPHr0iF69ejFz5kzGjBkDwLVr19i+fTs7duwQq85KsZiYGFxcXDA2NsbKyopbt27h4eFB7969ycjIkPeVDA4OZtq0aejq6vL69Wuio6Np0KCBosMvcaytralfv76YeBAp1NmzZ/H09JT3Zr1x4wbjx4/H3t6eyZMnk5WVJb5GvyK5w9/ebP/w5vC3NxO/pqamnzz87XNFRkbi7u6Oq6sr9+/fp23btowYMYIhQ4YUSs/1a9eu0atXL/r168euXbveeR6flJREjRo18PLyokePHl+8XZHoaycmhUWiz3TzWRxD/vqPwnjVKwEeP7cvsJ/R/fv3cXNzw93dnaioKL7//nvs7e3p169fvttl3icjI4NHjx7lSRS/mSzW1tbOlyjObUMhJotFHyMhIQEfHx8yMzPp1KkT9erVA2Dx4sWoq6sze/ZsJBLJey/GiESFycvLCxsbG2JjY6lYsaKiwymxhg8fTnh4OImJiZw7d46aNWuydetW5s2bx2+//Ubt2rVxcnJi5MiRLFiw4JPXv3fvXnbu3MmBAwfQ0dEpgj0QfYrIyEhOnz7NqFGjiIqKwsLCgv79+7Ny5Ur279/PvXv3sLOzQ1tbmzVr1hAcHExycjIWFhYsW7ZM0eGXGGlpaWhra+Pp6Unv3r0VHY6ojAoMDGTIkCF07tyZiRMncv78eU6dOoW3tzcAnp6ebNu2jfPnzys4UtGnEgSB0NDQPJW/vr6+eYa/vZkANjExQU9Pr1jP29LS0jh69Ciurq6cP38ePT09eZ/g3LuPCsO5c+fo168fjo6ObN68+b0FJgcOHGDMmDHExsaKF7FFoo8gJoVFoi+w1DsQl3+eI/mY8abvIEglNMgOxed3p/f2cJJKpVy+fBk3Nzc8PT2RyWQMHDgQe3t7unbt+tmJtoKSxYGBgTx58qTAZPGbbSjEZLHoTU5OTty7d49atWoRExPD6tWriYuLIzQ0FDMzM7799ltFhygqYzIyMqhevTq7d+/GxsZG0eGUOIIgIJPJGD9+PGPHjuXs2bOcPHmSv/76i1evXhEQEICfnx+JiYm0a9eOCRMmfNZ2pFIpXbp0QV1dnXPnzol3C5QQuXduSKVS2rdvT7ly5ejSpQvdu3enY8eO2Nra0qJFCzp37kzbtm2xsLBg4cKFDBw4UNGhlwgnTpxgyJAhxMfHo6WlpehwRGXU1atXGTVqFI8fPwYgMTGR77//HisrK5o2bcrhw4extrbmp59+UnCkovfJyMiQH3PfTAK/PfwtNwlcWMPfPodUKuXixYu4urpy+PBhlJWVsbGxYcSIEXTq1KnQj/FHjx5l6NChTJ06lRUrVnzw/HPYsGHIZDIOHjz43uVEIlEOMSksEn0BiVTGePe7XAiK+azEsKqyEqY1VTm/aCjWVj1wcXH5qNt70tPT8fLyws3NjVOnTlG9enWGDRuGvb09rVq1KpRkbW6y+M2q4reTxe/qWSwmi8sef39/pkyZIq9EmT17NqdPn0ZTU5NWrVoREhLC1q1bqV+/vmIDFZU5tra2qKqq4u7uruhQFC4zM5PDhw+TkpJCjx490NfXR0VFhTVr1pCWlsaCBQuwtbXF09OT1atXM336dGQyGTKZ7Isr/J88eYKJiQkrV65k4sSJhbRHosJy7Ngxpk6dio6ODv/88w+RkZH069ePDRs20KFDzjDdH3/8kZYtWzJp0iQFR1syjBkzhsjISE6ePKnoUERlXKVKldi0aRODBw/mzp07uLu7o6KiQnZ2Ntra2syaNYsqVaooOkzR/8sd/vZmAjgoKAipVEqdOnXytX8o7OFvn8vf3x9XV1f27dtHVFQUPXr0YMSIEfTt2xdNTc0i2aabmxuOjo4sWbKEOXPmfHD5rKwsatSowdatW7GzsyuSmESir42YFBaJvpBEKmPF6SB2XgtBWQk+JjesrASCAKM7NmC2VTMC/B/Qo0cPWrdujaen5ydVnMTFxXHo0CHc3Ny4ceMGzZs3x97eHjs7uyJJwGVkZBTYszg3WVytWrV8iWIxWfz1i4mJYdq0aYwYMQJLS0u8vb05dOgQLi4uJCYmsmTJErS1tZk7d66iQxWVMfv37+fnn38u87cRZmZmYm9vT40aNbh37x5NmjRh8eLF1K9fn507d/L48WN+/PFHhZBSUAAAIABJREFUunXrRv369alQoYL89uPCsm3bNn755Rd8fX1p0qTgfvui4pc7EKhVq1a8evUKOzs7srKysLW1Zfz48QwdOpTz58+zatUqZs6ciaWlJVKptEQkKRRFEAT09PSYN28e48aNU3Q4ojJu3rx5nD9/HnV1dZKTk9m2bRvt2rUjPj5ePkxMnOlQ/KRSKcHBwfLEb+7HN4e/vd3+oXr16ooOO4+oqCj5nJt79+7RunVrRowYwdChQ6lVq1aRbnvr1q1MmDCB9evXf/TF5LNnz9KrVy9iY2PFCyEi0UcSk8IiUSG59TyBhScCCHyZjJqKEtnSAn4fpBJQUcVQpxJL+rbI00P48ePHWFpaoq+vj5eX12cdyJ4+fcq+fftwc3Pj8ePHdOrUCXt7e2xsbKhateqX7N4HvZksfrO6+O1k8dvVxbVq1RKTxV+J3377jYSEBObPn49EIiEtLU3eP3Tp0qU8efIEFxcXBUcpKmuSk5OpUaMGx44dw9raWtHhKExKSgoTJkxg9+7dZGdn069fP/r06YOTkxMvXrygd+/ePHv2jF27dmFjY8OQIUOYP38+RkZGhRaDIAhYW1uTmJjItWvXxP7iJci///6Lubm5/HOpVMrGjRvx9vYmMTGR169f079/f2bMmMH58+e5cuUKsbGxbN26tcjfX5REucmR0NBQ6tatq+hwRGWcTCYjODiYJ0+e0LlzZwBu375N27ZtqVChgvg+uxgkJyfLh7/lJn8fPHhARkYGVatWzVP5a2JiQvPmzUvsherXr19z/PhxXF1dOXv2LDo6OvI+wYaGhsUSw+rVq5kzZw47duzA0dHxo5/n5OREcHAw586dK7rgRKKvjJgUFokK2YOIJE7ej+R26CuexKSSKZGirqqCQc0KZIY/JOSSBwHXThf43IiICCwtLVFXV+f06dOffQVWEARu376Nq6srBw4cICkpiV69emFvb0/Pnj3R0ND4kl38JJmZme9sQyGVSqlWrVqBbSjEZHHpkZaWxsWLF+nduzcRERHo6urKf3YRERFMmzaNsLAwVq9eTceOHRUcrags6tmzJ3p6evz555+KDqXYhYeHExUVha6uLr169WL//v00a9aMSZMmERMTwzfffEPjxo3x9vbmhx9+oH379kUaT0REBEZGRkyfPl28c6CYREdHU61aNdTU1D5q+YiICIKCgli+fDmhoaHo6+szf/58unbtyrlz55g3bx7r1q3Dy8uLM2fOcP78eapVyz8o92u2dOlSPD098fPzU3QoIpHczZs3mTVrFrdu3aJatWqoqqqiq6vLwIEDqV27NtbW1mXyIk5hyh3+9nb7h2fPngFgYGCQLwFc3MPfPodMJuPy5cu4urri6emJIAgMHjwYBwcHLCwsiq3KXBAE5s+fz+rVq3F3d2fw4MGf9Fx9fX1mz5792TMQRKKySEwKi0TFKCAgACMjI4KCgmjatGmBy8TFxdGzZ0/5FPh69ep90Tazs7M5d+4cbm5uHDt2DHV1dWxsbLC3t6djx44Ku5Xs7WTxm20oxGRx6bJv3z5++eUXlixZwujRo1FVVUUikaCsrExgYCAnT57E0tISMzMzRYcqKqP++usv5s6dy8uXL8vULe/Xrl3D1tYWR0dHli9fzuXLl2nVqhXZ2dk4OjrSrVs3Lly4gKGhIcOGDcPExASZTIaSklKR/p3dt28fI0eO5ObNm5iamhbZdkQ5d/G0bNmSXbt2fdRFudjYWCwsLDAxMaFOnTrMmDGDChUqoKWlRUJCAn369GH8+PEMHz4ciURCjx492LhxIy1atCiGvSk52rVrR7du3Vi+fLmiQxGJEASBjIwMfvnlF+rVq8dPP/3EuXPncHV1RUNDg3r16vHw4UN0dXXZuXOnosMtNTIyMggMDMzT/uH+/fskJiaipaWFsbFxnuSvsbExFStWVHTYn+Thw4e4urri5uZGREQE33//PQ4ODvTv37/YB2jKZDKmTp3Kn3/+yZEjRz757q7c6njxDg6R6NOISWGRqBgJgoChoSEODg7vbZafkpJCv3795Le/NG/evFC2n5KSwtGjR3Fzc8PHxwc9PT2GDx/O8OHDS8wJXWZm5jvbUEilUqpWrZovUWxoaEjt2rXFZLGCdO/enUaNGgFgZGQkDpESlTgxMTHUrl2by5cv06lTJ0WHUyy8vb0ZN24c/fv358GDB3h4eOTpVZiQkEC1atXkA8XmzZtHv379iiU2QRCwsbHh0aNH3L59u8TeQvs1WLp0KZs2bSI4OPij21I9fvyYxo0byz9/+fIlV69excPDg9DQUCZPnsygQYM4duwYHh4eLFu2rNDep5QG0dHR6OjocO3aNfkQPpFI0c6dO8fKlSvx8fGRP7Znzx4uX77M7t27gZw2c7nv10R5xcTE5Kn89fX1lQ9/09XVzVf9a2BgUGovMsfExHDgwAH27t3LnTt3MDU1xcHBgWHDhsnbvhU3qVTKTz/9hKenJ15eXnTp0uWT1zF//nz+/vtv7ty5UwQRikRfLzEpLBIVswULFnDy5Enu3r373uUyMjIYOnQo165d4/Tp07Rp06ZQ43j58iX79+/Hzc2Ne/fuyd8QDB06FF1d3ULdVmH42GTx29XFYrK46Onp6REeHk5ERASOjo5oamoyd+5c2rVrB+S80VNWVhZ/DiKF6ty5M2ZmZqxbt07RoRQ5T09PnJyc2L17Nz179mTy5MkMGzYMc3NzJBIJqqqqyGQy0tPTKV++PHZ2dtja2tK/f/9iizE2NhZjY2McHR1ZuXJlsW23LAkJCcHQ0JBt27bxww8/fPTzBEFASUlJ/lo5evQoO3fuZNSoUTRu3Jjx48eTkpJCs2bNMDU1Zfbs2UW4FyXPnj17mD59OtHR0aU2KST6+sTHx2NgYMCrV68AePbsGYMGDWLUqFFMnDhRHDT3/3KHv72ZAPbz8+Ply5eoqqrSvHnzfAngkjb87XOkp6fj5eXF3r175S0Khw8fjoODA8bGxgqNLSsrC3t7e86fP8+ZM2do27btZ62nZcuWDB48mAULFhRyhCLR101MCotExez+/fuYmJjw5MmTD16tl0gkjB49miNHjuDl5YWFhUWRxBQYGCgfUBceHk63bt2wt7dnwIABJf42qNxk8ds9ix8/fixPFhfUhkJMFheOCxcuMHXqVHlfRalUyh9//MHTp0+xs7OTJ4ZFIkVbt24dGzZsICQk5Kv93ZfJZAiCwKJFi+jVqxfm5uYIgsC4ceNQV1dnw4YNAKSmpnL58mWWLl1KzZo1efbsGXfv3qVcuXLFGq+Xlxf9+/fnypUrfPvtt8W67bKgX79+JCQkcOXKlS96zQ8fPhwNDQ35becuLi7Ex8czZcoUAJSVlUlKSiI+Pp5Lly6hra1dbFXnijB48GA0NTVxdXVVdCgiUR6Ojo48ffoUQRBo2LAhWVlZLFu2DAMDA6RSaZm7iPHm8LfcJLC/vz/p6elUrVo1T+LXxMQEQ0PDr+rOFZlMxrVr13B1deXQoUNIpVIGDhyIg4MDXbt2LRGvh/T0dAYNGsTdu3c5d+7cZyeoQ0JCaNiwIb6+vpiYmBRylCLR101MCotExUwQBJo2bcro0aOZNWvWB5fP7a+0fft2Dh06RN++fYssNplMxvXr13Fzc+PQoUNkZmbSv39/hg8fzvfff//RQ2pKgszMTB4/fpwnURwQEFBgsvjNpLGYLP40f/75J2lpaUydOpWMjAw0NDRITExky5Yt/PnnnxgbGzNlyhS6deum6FBFZdzz589p0KABd+/epVWrVooOp0jExsZSo0YN+efp6eloamoSERHB2LFjWbp0qbyHb1hYGNeuXUMQBIYPH66okBk1ahRXrlzB19eXChUqKCyOr83Jkyfp378/d+/epWXLlp+9HkEQmDVrFg8ePMDd3R1BEFi3bh3ly5dn9uzZXLp0iRs3bnD69GmkUikNGjRAR0eH1atXF+LelBxZWVlUr16dv/76iyFDhig6HJEoj/T0dC5cuIBEIiEoKIgqVarw888/KzqsIicIAmFhYfnaP7w5/O3tBLC+vv5X+37/0aNH8j7BYWFhdO/eHQcHBwYMGFCijrPJycn07duXkJAQzp8/n6dt0adav34969ev/6ov/ItERUVMCotECjBnzhzOnTvHrVu3Pmp5QRBYunQpS5YsYffu3Tg4OBRxhDlJ1b///hs3NzdOnjxJ5cqVGTp0KPb29rRt27bUHnDfTBa/WV2cmyyuUqVKgW0odHR0Su0+F5XcW4zf5fXr1yxduhQzM7NPmh4sEhWV1q1b06tXL5YuXaroUArdggULuHbtGlpaWjRo0IBNmzYBOdX7qamp/Prrr1hZWRXphcXPkZycjLGxMb169WLLli2KDuerkJ6eTosWLejbty/r16//4vVJJBI6dOhAXFwcenp6JCcnc/nyZdasWcOePXtISEigadOm/Prrr3n+1n/oGFEa+fj40KNHD+Li4j66R7NIpAjq6ur079+fuLg4TExM6NatGy1btkRfX79Ut5LIzMwkICAgX/uHxMRENDU1MTY2ztP+oTQOf/sccXFxHDhwAFdXV27evImRkREjRozAzs6OOnXqKDq8fOLj47G2tiYpKYnz58+jr6//ReuzsLDA1NS0UI55IlFZIyaFRSIFuHv3LmZmZoSEhFC/fv2Pft6mTZuYMmUK69evL9ZhXq9evcLT0xM3NzeuXLlC48aN5QPqDAwMii2OopSVlZWvZ3FgYCDBwcEFJotzP5blZLFMJiMkJISNGzdSp04d7Ozs0NPTQyaTIZVKUVNTIy4uDnV19TLxhlxU8i1btowDBw7g7++v6FAK1bZt29i6dSv79+8nJiaGkSNH4ujoyMKFC+XL7N69m+XLl3Pjxg2qV69eopICFy9epFu3bpw+fZrvv/9e0eGUeosWLWLbtm08evSIypUrF9p6XV1d0dHRwcTEhLNnzzJz5kxcXFwA8PX15enTp2zevBklJaUS89oqbFOnTsXX15eLFy8qOhSRqEBZWVmUK1eOsWPHMmLECExNTUlOTkZVVRWJREJwcDBZWVl0795d0aF+UO7wtzcTwEFBQUgkEnR1dfNU/5qampbq4W+fIyMjg5MnT+Lq6srff/9N9erVsbOzw8HBARMTkxJ7fhIVFYWlpSXKysqcPXuWWrVqfdH64uPjqVmzJufPn+e7774rpChForJDTAqLRAogCAKNGjXCycmJadOmfdJz3dzccHR0ZMGCBcyfP7/YD/ihoaG4u7vj6urKw4cPMTc3x97eniFDhnwVgxjelpssLqhnsUQioUqVKgX2LC4LyeLc10G9evUICgoiMTGRZcuW0bt3b0WHJhIVKCAgACMjIx49ekSTJk0UHU6hWbVqFTVr1mTkyJFAzu/mrl27OHDgAFWrVpWfJI8dOxZ1dXXWrl1b4k6cp0yZgqenJw8ePKBq1aqKDqfUevr0KS1atGDHjh3Y29sXyTZkMhmOjo706dMHGxsbAA4fPszcuXO5fv062traeZZ/9eoVVapU+SqOiU2aNGHMmDFMnz5d0aGIRB+UnZ2Nqqpqnt89iUQCgKqqqqLCykcqlfL48eM8rR/eHv72dvuHN1sllSWCIHD9+nV5n+DMzEwGDBiAg4MD3bt3L1E/14KEhobSvXt3tLW1OXXqVKEc7/fu3cuUKVOIiYkp8fsvEpVEYlJYJFKQmTNncvXqVW7cuPHJz/Xy8sLGxoaxY8eydu1ahVTkCIKAr68vbm5uuLu7ExcXh5WVFfb29vTt2xdNTc1ij6k4ZWVlvbNn8dvJ4jeTxl9Tsrhz586MHTsWOzs7IKc36LFjx1ixYgWjR49GVVX1q7x9WFR6fWpP95LuwIEDDB48mHXr1nHkyBH58WTx4sU4Ozvj5+dH8+bN5VXB7u7uVKxYkT59+ig48vzS09Np1aoVbdq0wc3NTdHhlEqCINC7d29SU1O5dOlSkf7tHT16NH379pUPlJs0aRJJSUnyyuGkpCQuXbpEeHg47u7uDBgwgClTppTqE/bg4GCaNm3Kw4cPadasmaLDEYlKpZSUFO7fv5+n9cODBw9IT0+nSpUqeSp/v8bhb5/ryZMn8j7BISEhfPfddzg4ODBo0KBSczdecHAw3bt3x8DAgOPHjxda3IMGDUJLS0sc/ikSfSYxKSwSKcjNmzdp164dYWFhn9VH6dKlS/Tt25eBAweyY8cOhZ5oSaVSLly4gJubG4cPH0ZZWZlBgwZhb2+PhYVFiatIK0pvJovf7ln8ZrL47epiXV3dUpc87dWrFwsWLKBdu3ZAztC5HTt2oKmpyfTp00tk4kkkyh2O9e+//yo6lC/SqVMnDAwM2L17N69evWLMmDGEh4fTqlUrJBIJZ8+eZeTIkSxcuFA+db4ktYwoyM2bN+nQoQMHDx5k0KBBig6n1Dl+/DiDBg3C19cXIyOjIt3Wrl27mDNnDmvXrmXXrl2YmppSuXJlKleujLe3N7Vr1yYwMJDKlSsjkUiYNm1aqT8mrF27li1btvD48eNSd7wWid5W1BftBUHgxYsX+ap/nz59CkCjRo3yVP6ampp+1cPfPkdCQgIHDx7E1dWVGzduYGhoiIODA8OHD//iHrzF7f79+1haWtK2bVs8PDwKrXgoPT2d6tWrs3fvXvF9g0j0mcSksEikIIIgUK9ePaZNm8bkyZM/ax23b9/GysqKjh07cuDAATQ0NAo5yk+XlpbGiRMncHNz48yZM9SqVQs7Ozvs7e0xMTFRdHgKU1CyOLdnsUQioXLlygW2oSjJyeLDhw8zY8YMxowZg4GBAbt27aJdu3bo6enh7OzM4sWLi2Uookj0Kf777z/Mzc0JDw8vkcNXPiQ7O5t27drRtGlT9u/fL388JiaGtWvXkpqairm5OY8ePSIsLExeuVlazJ8/n61btxIQEPDFfQbLktevX2NoaMigQYNYs2ZNsWzT1dWV//77j+zsbCZPnoylpSUdOnRg9uzZNGnShL///pvHjx/TuHFjhgwZAuS8ftXU1IolvsLWrVs3jIyM2LBhg6JDEYlKlMzMTAIDA/MlgN8c/vZm9W/Lli1LTXVrccsd9O3q6srJkyepWrUqw4YNY8SIEbRq1arEnhO8z3///YeVlRU9evTA1dW1UI8BJ0+eZPDgwcTFxVGhQoVCW69IVJaISWGRSIF++eUXbt26xdWrVz97HQ8fPuT777+ncePGhXorTmGIiYnh4MGDuLm5ySfh2tvbY2dnV+qucBeV3GTxm4nigICAEp8sjouLo3r16nh5eeHu7s79+/exsLBg1apVVKhQgbNnz+Lh4cFff/2lsBhFooLIZDL09fWZM2cOTk5Oig7nk/n4+ODs7MyZM2dQU1Pjt99+QyaTYWBgQN++feUnW6mpqbRv354jR47QuHFjBUf98bKysjA3N0dPT4/jx4+XyhNgRZg/fz47d+4kKCiISpUqFdt236w+9/LywsnJiWXLltGmTRv27t2Lvr4+48ePl/8cc6vWAwICaNGiRbHF+aWSkpKoXr06f//9N5aWlooORyQq0JvVv0lJSdy9e5fIyEg6duxIvXr18iwrk8lITU395L8XsbGxeRK/fn5+PHz4EIlEgo6OTr72D40bNy5Tdwx+DkEQ+Pfff3F1deXgwYO8fv2afv36MWLECCwtLUvthTTIGSTbp08fhg4dyvbt2wv9tfDTTz8RGRmJt7d3oa5XJCpLxKSwSKRA//zzDx07diQ8PBxdXd3PXk9oaCiWlpZUrlyZU6dOlciBb8HBwezbt0/eC6tLly7Y29szaNAgqlSpoujwSpysrCyePHmSr2dxQcniNz/WqVOnWJIoY8aMYdmyZdSsWROpVIqysnK+7b569UocGCUqkSZMmEBQUBDnz59XdCgfLfdkPyMjg7Fjx9KiRQsiIyM5f/483377LbGxsUgkEg4ePIiGhgYRERGMHTuWv/76i9q1ays6/E/i7++PmZkZ27Ztkw/PE73b48ePMTIywsXFhaFDhyokhtwK4Js3b9KvXz+aN2/OyJEjsbOzQ0VFBX9/f8LCwnB1dSU6Oprs7GyOHDlSaoZFeXh4MGrUKOLi4sT+pqISJywsjKpVq1KxYkVevHjBjBkz8Pb2pmHDhnTr1o0pU6ZQt27dPM/JLUp418WZ3OFvbyeAIyMjUVFRoXnz5nnaP5iYmFCzZs3i2N2vxrNnz3Bzc8PV1ZUnT57QpUsXRowYwaBBg6hcubKiw/ti3t7eDBo0iHHjxrF27dpCPz+RSqXo6uqydOlSxowZU6jrFonKEjEpLBIpkEwmo27duvz6669fXLEWHR2NlZUVmZmZnD17Fj09vUKKsnDlXg13c3Pj4MGDpKam0qdPH+zt7bG2tqZcuXKKDrFEy87OLrBn8dvJ4reriwszWfzw4UO6d+9OREQEMpmM8+fP4+3tzfPnzzEzM+PHH3/8ooscIlFR8/HxoUePHkRHR6Otra3ocD7I398fNzc3GjZsyKBBgxAEgVatWqGurs6TJ0+AnCE006dPp2/fvowaNQr4X0V/afTbb7+xdOlSHjx4kK/CTfQ/giBgbW1NVlYWPj4+Cq+sDg4OZsuWLRw6dIjFixdjZWXFxo0b2bZtG1paWtja2jJx4kR0dXXR0tIq0T2u3+To6EhycjJHjhxRdCiiMiwtLQ0NDQ1UVFTIzs5mxYoVLFq0CB0dHQYOHMimTZt4+vQpc+bMYd26dR98LyaRSPLMJElOTmbGjBn4+vrmGf72ZuVv7vC3ktCyrjR69eoVHh4euLq6cu3aNZo2bcqIESMYPnz4V3WsO3jwIPb29sydO5eFCxcWybHpn3/+4dtvvyUyMhIdHZ1CX79IVFaISWGRSMEmT56Mn58fly5d+uJ1JSYm0qdPH168eMG5c+dK/C3DWVlZnDlzBjc3N44fP0758uWxtbXF3t6eDh06KPzktjTJTRa/mSgODAzk0aNHSCQSKlWqVGAbis9JFi9evJj79+9z+PBh9u/fz9KlS+nbty8tWrTg8OHDNG3alFWrVhXRnopEXy47O5vatWuzdu1afvjhB0WH81537tyhbdu2ODg4cO/ePSwsLNi4cSNXrlzh5MmTODs7o6KiglQqxdbWltatWzN//nxFh/3FpFIpFhYWqKmpcf78+VKTPCxuR44cYciQIfj5+WFoaKjQWGQyGS4uLoSHhzNkyBD+/fdfHjx4wD///EPbtm0RBIHMzExWrVolr4Ir6cMPIee1qKOjw8qVK+UXXESi4pKdnc306dPZvn07DRo0YMKECTg5OXHp0iUmTZrEmTNnSE9Pp3fv3ixbtgxra2vMzc25fv06AQEBZGVl0alTp4/e3qRJk6hevbo8CVy3bl3x/fgXysrK4vTp07i6uuLl5UXFihUZNmwYDg4OtGnT5qv7/u7cuZMxY8awevVqpk2bVmTbmTVrFleuXOHGjRtFtg2RqCz4mKQwgiB88F/OYiKR6FNduXJFUFJSEqKiogplfWlpaYK1tbVQq1YtwdfXt1DWWRwSExOFXbt2CV27dhWUlJSEBg0aCPPmzRMePnyo6NBKtaysLCEgIEDw8PAQFi1aJNja2gotWrQQ1NTUBECoVKmSYG5uLowePVpYs2aNcPr0aeHFixeCTCZ75zpHjx4tjB07VhAEQXB0dBS2bdsm35a3t7dgbm4u+Pj4vPP5W7duFZ4+fVq4OyoSfaIffvhB6Nevn6LDeKfc38EVK1YI06dPFwRBEO7cuSM0a9ZM+PvvvwVBEITXr18LgiAIcXFxgiDk7NPq1asVEG3RePLkiVC+fHlhw4YNig6lREpNTRX09fWFGTNmKDqUPF68eCEIgiCcOHFCsLa2FgICAgRBEIQbN24IHTt2FIKDgwVBEISoqCjBwcFBWLRokbBq1SqFxfshN27cEADh5cuXig5F9BXLyMgQxo0bJ9SsWVPo2LGjcP36dUEQBOHYsWNCly5dhIcPHwo+Pj6CkpKS8OLFC+Gff/4RDh48KH9+//79hd27dwuCIAg1a9YU6tatKzRt2lRo3bq14OLiIqSnpytit8osmUwm/Pfff8KECRMEbW1tQV1dXbCxsRFOnDghZGVlKTq8IrNu3TpBWVlZ2L59e5Fvq2nTpsKKFSuKfDsi0dfu/3O57833luzL9yJRKdehQwdq1arF0aNHC2V9WlpaHDt2jO+++44uXbpw/fr1QllvUatcuTIjR47Ex8eHsLAwxo0bx4kTJ2jevDlt27Zlw4YNREdHKzrMUkdNTQ1DQ0MGDx7MwoULOXjwIP7+/qSlpREYGMjOnTuxtrYmJSWFXbt20adPH/T19alSpQrt27fn8ePHuRf+5H744QekUikmJia4uLjIhxuqqanRs2dPlJSUSEpKKjCeiIgIvL29+f3330lISCjy/ReJ3mXAgAGcOXOGtLQ0RYfyXhKJhNTUVKRSKa1bt8bGxoZTp04BOdVH3t7e2Nra0qtXLy5evMiPP/6o4IgLT6NGjVizZg2zZs0iKChI0eGUOM7OzshkMhYsWKDoUPLIbV8VFBREu3bt5BXM+vr6JCcnExUVBeS0RWnVqhXffPMNt2/fZtu2bQqL+X28vb1p06ZNqevNLSrZkpOTad68ufzzvXv3EhkZiYeHB1ZWVjg4OJCVlUVsbCyzZ8+mWbNmdO3alQYNGvDs2TNatWqFjY0NMpkMyGndktvTd8CAAbi7uxMUFMTs2bM5cOCAvJryzfd0MpmMO3fuFONef/1CQ0NxdnamWbNmtGvXDl9fX1asWEFUVBSHDh2iT58+pXpw3LsIgsCSJUuYMWMGbm5uRd7jNygoiEePHtGvX78i3Y5IJPp/H8oaC2KlsEj0RcaPHy9069atUNcpkUiEsWPHCpqamsKpU6cKdd3Fyc/PT5g5c6ZQp04dQUVFRbCyshLc3NyE1NRURYf2VcrKyhICAwMFDw8PYcWKFYIwvHT6AAAgAElEQVREInnnss+ePROWLl0qrFmzRhAEQfD19RXWrVsn1K5dW0hOTs63fGZmphAeHi4IgiD07dtXXu34rqrk3bt3CwMGDBAWLVokREREfOmuiUR5vH79WtDS0hI8PT0VHUo+v/32mzB48GBBEHKqZQMDA+Vf27hxo9CjRw/551FRUcKZM2eEHTt2yCuHvyYymUywsrISvvnmGyE7O1vR4ZQYQUFBgpqamnDo0CFFh/JO+/fvF0xNTYUXL14It27dEsaPHy+YmZkV+Dd/5syZwqJFixQQ5YeZmpqW2NhEJdezZ88EZ2dnYejQocLJkyfzfX3z5s2CkpKS/H3Rzz//LFy+fFkQBEFISkoSGjduLPj6+grJycny35mkpCTBzMxMuHjxoiAIgiCVSgVBEAQPDw+hV69e8ir9lJQU+XaSkpKEn376SV5R+ebv3/+xd+cBMef/H8Cf0+GI3OWWIklR7lwRJaXShiiF3OVYZ+5jXes+1n2to+S+E8JKbkJRbpUkotB9TDPP3x/9+qwWu7671XR8Hv/QNDPv10wzn+P1eb1f76ysLG7evDnPZiuWVJ8/f+b27dtpampKAGzYsCHnz5/P8PBwRYdWIORyOSdPnszSpUvzxIkTBTLmkiVLqKur+7czG0Ui0Y/BD1QKq/x9ylgkEv1Xffr0gYWFBT58+JBnK3ErKytj48aNqFKlCuzs7ODl5YV+/frlyXMXpGbNmqFZs2ZYvHgxAgMD4e3tDQ8PD4wcORI//fQTXFxc0K1bt1wLZYj+PVVVVejr6+eqXvnSkydP8McffyAtLQ1WVlaYNWsW5HI50tLSEBwcjODgYOzYsUOoHv7rc+dUsbRr1w7Jyclf9ZMkCYlEggkTJiAqKgpNmzbFqVOnkJmZiVmzZqFs2bI4ceIE9u/fjxo1asDd3R2NGjXKnzdDVKyVLVsWVlZWOHr0KHr37q3ocATLli3Dtm3b4O3tDSC7WjanEgwAzM3NceLECeHnw4cPw8XFBd27dy/wWAuCRCLB9u3bYWhoiCVLlmDWrFmKDknhSGLs2LHo3Lkz+vTpo+hwvqt///548OABunTpAl1dXVSoUAFHjx6FRCJBZmYmrl69isqVK+PJkydISEgQFsVKS0sDkP0dVbTo6GgEBwdj+/btig5FVEglJCTg8OHDiIiIwOTJk1GpUiUAwJIlS5Ceng4LCwvMmjUL79+/x6BBg6CkpAS5XI5r165BSUkJL168QO3atTFx4kQ0bNgQQPaCvk2aNIFMJoO6ujpkMhmUlZXh5eWFevXqoXXr1rli8PX1ha6uLurUqYPExETExsZCWVkZOjo6iIuLw4sXL9C+fXsAyNW/VklJCfr6+nj+/DmqV69eQO9Y8SCVSuHv7489e/bg5MmTUFNTQ//+/bF06VK0bdu22PUJ/h6ZTAYPDw94e3vj9OnT6NatW4GMe+LECdjb25eY91kkUrh/yhpTrBQWif4TqVRKDQ0Nbtu2LV+ef/ny5QXW36kgpKam8uDBg7Szs6OKigqrV6/O8ePHMygoSLxinM9atmzJ9u3bs0+fPjQ0NOS6detIZlcYZ2Zm/m1Fb04PNalUyrVr1wq9iL9l69atQvXM7du3herjffv20dTUlLNmzaKVlRV79+7NtLQ0nj59mm3btqW5uTknT57MQ4cOlZgKDdG/5+3tzYoVKzIjI0PRoZDMjkcikfDjx48kyTdv3jAmJibXfe7evcvWrVszPj6eZmZm7N+/vyJCLXA+Pj5UUVHhvXv3FB2Kwh08eJCqqqpFpuf+o0ePmJSUxKysLC5ZsoQhISEkyR07drB06dLs1q0b165dy0+fPjE0NJQLFiygtrY2Q0NDFRw5uWXLFtaoUUOoyBSVXJ8+feKpU6f422+/CRW5JDlv3jyamppy0qRJdHd35+PHjxkUFEQrKytGRkaSJFesWMGhQ4cK/bW3bdvGmTNncvTo0ZwzZ47wXDnHsIsXL6atrS1JCjO2EhMT2b9/fy5atIjkn8dUAQEBNDAwyPUZ3bJlCy0tLRkREcElS5awe/fuQu/5v3r37h1TUlLy5D0q7uRyOYOCgjhu3DhqaGhQVVWVDg4OPHbsWKE5jihImZmZdHZ2ZsWKFYUe2AXh3bt3lEgkvHr1aoGNKRIVZ/iBSmExKSwSFYARI0bkmhKc17Zv304lJSUuWbIk38ZQhA8fPnDjxo1s3749AbBx48ZcuHChmBDMB3fv3mW5cuWEn48cOUILCwthuiNJOjk5fZXEIrOnOOacRMXHx3P58uXcunXrd8fKOQmSSqX8448/qKOjQzJ7wZWcBRRTU1OFBVdI8uHDh9yzZw89PT1Zt25dGhgYMDExkREREXRzc+OoUaP4xx9/fDWWeCGh5Pr06RNVVVV59uxZRYdCkvz48SO7dOnCBw8eMCAggLq6umzbti0dHBwYFhbGrKwsRkREUE9Pj7q6unR1dVV0yAVGLpezb9++NDQ0LNELJiUlJbF27dqcNm2aokP5n8XExLBx48b8/PkzSTIsLIz29vZCu6GzZ89y8ODB3LFjB0ePHk0tLa1c+xdFsLW15dChQxUag6hgyWQy3rlzh3fu3BGOReRyOYcMGcI2bdrQzs6OLi4uvHPnDuPj49m6dWuhTdvPP//M7du3c+/evXRzc2NCQgJJ8sSJE7SwsGBERATJ7FYRu3fv5qlTp2hjY0PyzyRvXFwczc3NuW/fPmFskjx9+rRwLJRDKpXSwsKCCxYsYHBwMNetW8f9+/czMTGRnp6erFmzJs3NzRkQEPDd1yseA/2zqKgo/vrrr9TX1ycAtm/fnps2bWJ8fLyiQ1OYtLQ02tnZUUNDg/fv3y/Qsbdt20YNDY2/bXEnEol+nJgUFokKCX9/f6qoqOTrAcahQ4eoqqpKT0/PYnkQ+OLFC86fP5+NGjUiAHbs2JGbN28u0Qdteen69evs1KkTnz59Kty2atUq6urqMjExkVFRUaxRo8Y3HyuVSnnlyhWS5OPHjzl9+nSeOnWK5N+fkDx+/JgWFhZC3+Ive4o+ffqUtWrV4ocPH756nKurK0+ePEmpVMp27dpxx44dnDJlCrt27cpXr159df/AwEDh5E1UslhaWnLEiBEKjSEhIUGo4tq/fz8dHByoqanJI0eOMCYmhv369eNPP/3EjIwMymQy1qtXj6NHj1ZozIrw4cMHVq9enVOmTFF0KAqTc9GrKPbVT0xMZIsWLbh7925GRERw69attLOz48ePHxkQEEA9PT2amJjw/fv3JLMr501MTBgTE6OQY5bU1FSWLVuWx44dK/CxRQUjOjqahw4dor+/P9PT00mSY8aMYe3atWlvb8+dO3eSJA8cOMCJEycKjxsyZAjnzZvH4OBg2tvbC8dFvr6+9PT05NmzZ2ltbc1p06bx3r17tLGxYcuWLUlmX2B3dHQkScbGxtLQ0JCxsbFCT/gdO3YI982RlpbGNm3a0M/Pj69eveLhw4cZHBzMO3fuUCKRsFq1amzWrBl/+uknoafr/1Ld/urVK+FijShbYmIid+7cSTMzM0okEuro6HDu3Ll8/vy5okNTuKSkJHbr1o21a9dWyIyVnj17csiQIQU+rkhUXIlJYZGokMjMzGSVKlWEA9D84u/vTzU1NQ4bNqzYXmGVy+W8fft2ruld9vb2PHz4cImuMPuv0tPTOXbsWK5fvz7X7SNGjODw4cM5ZcoUDhw48LuPzzmhmjt3Lj08PISKmb+e7Of8/PTpU/bp04eDBw8WfpdzkvP69WsOGjSIAwYMIPlnsvjjx49ctWoVe/XqRTI72WthYSE8/svFVnJcu3aNFStWFJLWcrmcU6dO5eDBg7l161bhRFFUPG3ZsoXVq1dX2PbQ0dGRtra2NDMz44ULF5iQkMABAwZw1qxZwn3kcjmrV6/OjRs3ksyuii+pTp06RSUlJeH7WpI8evSIKioqPHLkiKJD+dfu3r3Lli1bskePHuzfv79QDRkbG8tr167R3d2dRkZGwkJZb9++VVisfn5+LFWqVK5Fu0RFU3p6utCWh8yuStfR0aG2tjabNWsmtLM6cuQIW7ZsSZlMxsePH9PDw4NBQUE8f/487ezshMdPmDCBixcv5suXL9mxY0fhc/rq1SsOGjSIDx484P79+9m+fXsOGTKENWvWFKr716xZQw0NDfbr14/6+vpUUVGhtrY2X7x4wfj4eJqYmDA4OJhpaWm8fPkyExISePjwYUokElauXJkNGzZkq1at+Mcff/DDhw+5Wln8G1lZWezdu7fQrqIkk0qlPHPmDJ2cnFi2bFlWqlSJI0eO5NWrV4tlMc2/8enTJ7Zr1446OjrCcXxBSkpKYunSpXny5MkCH1skKq7EpLBIVIgMGTKEPXv2zPdxrl+/zkqVKrFv377FPuGVmZlJPz8/Ojs7s2zZsqxYsSKHDRvGgIAAsUfgv5CamsrHjx9TJpPlqtrt2bMnJRLJN1fYznmco6MjBw4cyNatWwtVwn+Vc9AdFhZGMzMzuru7CxWUOUm7T58+0c3Njf3792dsbCzJP6ddent708nJSaja8fb2ZocOHUiSz54944gRI7hmzRphvODgYDZo0IBGRkaMjo5mUlISV69ezZEjR3LlypW0trbm77//TjI7IX3p0iUOGDCAGzduLLYXVUoaRfamc3Fxoa2tLcPDw7l582a2adOGiYmJDAgI4PXr13Pdd+jQoTxw4ECBx1gYDRkyhDo6OiUqWSeXy9m1a1daWloW+eREQkIC379/T5lMxtu3b3P58uW5XpOZmRnPnz9P8s99Qs42viB5eHiwe/fuBT6u6L/58tgkKCiI7du3p6amJgcMGCAkkry8vNitW7evHvv777+zbdu2JLOTT0ePHqWnpyeTk5Npb2/P8ePHc+PGjdTT02NcXByTkpLYqlUrPnv2jGT2cYqTk5Ow/Y6NjeWVK1dYrlw5ofXVunXrOH36dG7atIkRERFs0aIFX7x4QTL7orlEImHNmjWppaXFGjVq8P79+3z+/Dnv3buX59u8jIwMJicn89KlS6xWrZpCvmeKJpfLef/+fU6YMIHVq1enqqoqe/XqxcOHDxf7c6T/VWxsLI2NjdmkSZO/XUMkPx05coRqampCZb1IJPrvfiQprPTXhedEIlH+6NOnD/z9/ZGQkJCv47Rr1w6BgYG4cuUK7OzskJKSkq/jKZKqqiqsrKywd+9exMbGYt26dYiKikLXrl1Rv359TJ8+HWFhYYoOs9Dz8fHB7NmzUbZsWTRu3BhKSkpQUVFBRkYGAGDixIkwMzND9+7dv/n4smXLwsPDAzo6Oti2bRtsbGwwfPhw7NmzB1lZWQCyL0BKJBLcv38fbm5ucHJywsaNG1G1alUAgLKyMtLT0+Hk5ITU1FTMnz8fmpqayMrKgqqqKuLj43Hu3Dm0b98ejRo1AgDo6elBWVkZffv2xfjx40ESLi4uAIALFy5g8+bN0NfXh52dHdTU1PD8+XNERERgwoQJmDhxIvr374/Vq1cDAA4ePAgPDw80bNgQt27dgq+v71evMyUlBVevXs3bN1+Ur6pXr4727dvj6NGjBT62trY21qxZA21tbfz000+oWrUqUlJS0LJlS7Rr1w5+fn54/PgxTp8+DV9fX3F1+P+3evVqyGQyTJkyRdGhFJgDBw7g6tWrWLduXZFfbb1ChQrQ0NCARCKBl5cXMjIycr2m1NRUpKWlAYBwu6qqKqRSKR49elQgMZKEr68vbGxsCmQ80Y/LPn+FcOwAAG/evIG1tTV0dHSwatUqyGQypKSkYO/evbC1tUVsbCzMzc0xZswYAICurq7wXJcuXcLz588BAOrq6qhWrRoAoHz58mjUqBFCQ0NRrlw5TJkyBRcvXkR4eDgGDhyI4cOHQ1VVFbq6urh37x5kMhmUlZXx+vVrJCcnAwA0NTVRqlQpWFlZoUqVKgCAMWPGYPHixRg1ahTq16+PBg0a4MqVKwCADh064OTJk7h+/ToiIyPx9u1bGBsbo2HDhmjevDnKly+fp+9hREQEIiMj0aVLF+jr6+PIkSN58vxFwZs3b7Bs2TI0a9YMzZs3x/Xr1zF79mzExMTg+PHj6N27N0qXLq3oMAuN6OhomJqaQllZGZcvX0atWrUUEseJEyfQvXt3lC1bViHji0QllZgUFokKSLdu3VCuXDmcOnUq38dq2rQprl69iufPn8PCwgKfPn3K9zEVTV1dHa6urjh37hyio6MxYcIE+Pv7w9DQEM2bN8fKlSsRExOj6DALpX379qFMmTIAgPDwcEyfPh2DBg3C+vXrERERga5du2LPnj1QVVX97nN07twZc+fOhZGREQBgw4YN6NWrF1RUVAD8efI/fvx43LlzBwcOHICnpyd27NgBqVSKz58/w8zMDCYmJti/f79wUqeklL2b+v3336GpqSmcxKempmLx4sVwcHDA4MGDoaWlhU6dOqFq1aoIDQ3Fxo0b0a5dO/Tt2xdKSkpQVVWFmpoa4uLihO/DvXv30K5dO7x58wZ+fn5wcHDAvHnz0KtXLxw8eBAAIJVKAQDJyckYNWoURowYAeDPE1aZTIbbt2/jxo0befTXEOU1BwcHHDt2TDhRzk9fjpGWloZVq1bh8+fPiImJwYMHD7Bs2TI0atQId+7cQXR0NMaPH48ZM2Zg6dKl6Ny5c77HVxRUqFABu3btwpYtW3D27FlFh5PvkpKSMHHiREyZMkXY7hUHEokEtWvXxpkzZxAXF4fQ0FD069cPcrkctra2ALK3wRcuXMDYsWPRtm1beHh4IDAwMN9jCwsLQ1RUFHr27JnvY4n+WU6xhEwmg0QiwR9//IEWLVoAANLT07Fv3z6oqanhzp07SElJweLFiwFkX0xxd3cHAAwcOBAymQyPHz9GcnIyHjx4AC0tLYwdOxbDhw/H06dPUa1aNZQpU0Y4BqhRowbkcjkSExMxe/ZsbNy4EcuXL8eMGTPw5s0bnDt3Ds7Ozjh+/Dj8/f0RExOD5ORkZGZmCrGvWrUK9evXR926dSGXy4XXkXOM4OPjg8GDB0Mul8PCwgI2NjaoX79+vryPOeNLJBLI5XKMGDECwcHBAIAhQ4YIyfHiKjk5GV5eXrCwsEDdunWxadMm2Nvb4+nTp7h58yZGjx4tXBQQ/enly5fo1KkTNDQ0cPHiRYW9R1lZWfD19UWvXr0UMr5IVKL9UykxxfYRIlGeGTRoUK6+ZfntzZs3NDQ0ZNOmTRXau0+RwsLCOGPGDGppaVEikdDc3Jy7du0SVkQXkdra2sI0unbt2tHd3Z1Tp06lubk5K1asyAsXLuTZWDKZjC9evODevXs5adIk2tjYMCUlhRMmTKBEImHbtm05ZswYbt26NVePQF1d3Vx9Rv39/WlrayssnvLu3Tt27dqVYWFh7NGjB48ePUqSXL9+PXft2iW0g7C3t2eXLl3Yv39/WllZ8d69e7x06RKHDh3KoKAgkuSZM2e4aNEiRkdHk8xuj7Fy5UpKJBLOmDGDZPa0TJI8fPgw27Vrx7Vr1wqxvX79mhMmTODQoUN5/vx5sZWJgoWHhxNAvq+gHRMTw/nz53Px4sX08/OjXC6no6MjHRwcWL58eaE9xJo1a6ilpcWMjAxKpdJcn3PRn8aPH89atWoV+/dn0qRJ1NLSYkpKiqJDyRdOTk5s1aoV27dvz7Zt2/LVq1ecNm0aR48eTWNjY44cOZL9+/enpaUlN2/ezIyMDGG6fX759ddfqa+vn69jiL4mlUoZHh7OJUuWcMCAATQyMqKmpqawMFsOZ2dnli9fXniMoaEhz549SzK71YixsTHlcjlVVFQYHh5OMrs/dYUKFfjs2TNevnyZkydPFo4PnJycOHXqVF67do1OTk5CO6GYmBgOHDiQISEhtLe359KlS4UxW7RowcWLF5Mkp0+fThsbG3bp0oXTpk0T2jDI5XL6+fkVij7wO3bsoJmZGdevX8/z58+zV69edHR0FBatLK7HIVlZWfT396eLiwvV1NSENnKBgYHF9jXnpdDQUNasWZOWlpYK3wddunSJSkpK31xgWiQS/Xv4gfYRKopNSYtEJUufPn3Qp08fJCYmokKFCvk+Xq1atXD58mVYW1ujY8eOOH/+PLS1tfN93MKkSZMmWLRoERYsWIBr167B29sb48ePh7u7O3r16gUXFxd07979b6tgi7OHDx8iMjISBw4cgK6uLiIiInD9+nXh9/Pnz8exY8fQqVMnlCpV6j+Pp6SkhAYNGqBBgwZwdnYWbl+1ahXGjx+P+/fv4/bt20KlUMuWLXHx4kXExcWhY8eOwv01NDRw48YNfPjwARUrVsTNmzeFasy7d+9i+vTp8PHxQWRkJExNTSGTyeDp6YlmzZrByMgIT58+RVJSEpo3b45169ahZs2aqFu3LoDsKp+MjAyh6mbcuHGoX78+mjZtKlQvlSpVCpcuXRI+T46OjgCyK9+WLVuGUqVKQUlJCTNnzsSKFSvQqVMnfPjwAbt27UJwcDDs7e1ha2srVGgDf7bYEOUtbW1tGBkZ4dixYzA2Ns6XMRITE+Hu7g4DAwPExcXh4cOHqF27Nry9vfHq1StoaGgInxEbGxvs2bMH0dHR0NHRQeXKlfMlpqJu8eLFOHv2LMaMGYO9e/cqOpx8ERoaijVr1uDIkSNQU1NTdDj5wsfHB2FhYahYsSIqV66MwMBALF26FJcuXcL69esRHByM69ev4/Pnz3B0dESpUqVw5swZeHt748KFC3k2pf5LYuuI/PfmzRtcuXIFd+7cwd27dxEbG4vBgwejatWquH//PkxMTODh4QFvb2+YmJgIj4uJicHr169RqlQpvHv3DjVq1EBMTAxq1qwJILvVSOnSpfHq1St4eHjAzs4ODg4OuHTpErS1tVGuXDmYmpqidevWwv510KBB8Pb2RqVKlaCpqYlDhw6hQ4cOuHr1KuLi4tCoUSN07twZ27Ztw4cPHxASEoIKFSrA3t4eALBw4ULcvHkTL1++hIWFhXC8KJFIYGVlVcDv7Lf17dsXZcqUwebNm/Hq1Ss4OTlh+PDhKFeuXIHMkiloDx48gJeXF3x8fPD+/Xv06NEDO3fuhK2trdh64AfdvXsXlpaW6Ny5M3x8fBTeTuPEiRPo2LGjWM0tEinCP2WNKVYKi0R5Jj09nerq6vTx8SnQcZOSkmhubs5atWoxNDS0QMcujNLT03n06FE6ODiwVKlSrFatGseMGcObN28W+UV+/hc5r/XixYucOnUqe/bsya5duzIlJUVYzCUwMJCNGjVSZJhMSUnhgwcPSP4Zc2pqKocMGcIGDRrQxMSExsbGXLJkCUkyOTmZd+/e5aZNm9ikSRO2atWKXl5e1NfX56NHj4Tnbdu2LW/cuMHVq1dzwYIFwsIWBw4c4MyZMymTybhkyRI6ODgwKSmJHTp04M2bN0lmL8jRokULtmvXjrt27RIqra9fv05/f3+hOqdNmzZcsGABSdLR0ZF9+/bluHHjqK+vzyNHjpDMruDJqWQOCQnhq1ev8vX9LIl++eUXNm3aNN+ePzk5mW5ubkxOTuanT5/o6urKHTt2CL+rU6cOly1bxvfv33P48OFs2LAh4+Pj8y2e4uL27dtUVlbmoUOHFB1KnpPL5TQ1NaW1tXWJ2u+Q2d9HPT09+vr6cv369ZwzZ85XK927u7uzadOmfP36dZ6OHR8fTyUlJQYEBOTp84qyZ0HkzLgZMWIELSwsOGnSJJ47d46NGzfm5cuXv3pM27Ztc1XaDhs2jPv27cs146dDhw7cs2ePcB8XFxdu3bqVJLlz504uWrSIDRo04JAhQ0hmz5J7+PChsF/+/fff2apVK2ZmZvLGjRu0sbHhiBEj2K9fP86cOZNk9nHyvn372KtXL06ePJm3b9/Oh3co/yUmJjImJoY+Pj589OhRrsXUinrlbExMDFesWEEjIyMCYKtWrbh27VphUWLRjwsMDKS6ujoHDhyYa/FGRZHL5dTW1ubKlSsVHYpIVOzgByqFxaSwSFTABgwYQAcHhwIfNz09nQ4ODqxSpQpv3bpV4OMXVh8/fuTWrVtpampKAGzYsCHnzZvH58+fKzq0ApWRkcHff/+dI0aM4NOnT5mZmckHDx6wX79+HDx4sKLD+6bk5GSeP3+eq1atEhKsXyZXPn/+TBcXF8bFxTE5OZkdOnTghg0bSJIPHz5k586d6evrS19fX7q4uAiP+/XXX7ly5Ur6+fnR3NxcuF1XV1f4v6urK6tWrcpJkyaxX79+nD9/vtBSgqSQ5DU0NOSBAwcYGRnJ48ePCy0pXF1dOXbsWKalpQmPefz4MatWrco1a9YI8U+ePJl9+vThihUrePny5VxjiH7cgwcPCCDPv9c504hDQ0NpamrKd+/ekST79+/PDh06cOLEiTx27Bjv37/PihUrsn379mzQoAFfvnyZp3EUZ3PmzGHVqlWLXQskb29vli5dOt9bJRQmOdtFkpw7dy4rVKhAV1dXxsXFkczefn+ZoJgzZw61tbX5+PHjPItBJpPxypUruWIR/XepqamsUKECN27c+M3f9+vXT7i4m7MfCwgI4OjRoxkZGUkyO5lra2vLZ8+eccaMGZw3bx5JcvTo0Rw1apTwXKtXr6azs7Pwc0xMDN3c3IT9+7Nnz+jm5sZly5bx0aNHHDRoEMePHy/c/927d5w2bRpnz55drBKKOZ/pdevWUSKR0NPTk3p6ely4cCE3bNjAhIQEoaVGUZGcnExvb29aWlpSSUmJdevW5fTp03Nd4Bf9b86ePcuyZcty9OjRheZCQc4xWknaH4pEBUVMCotEhdCxY8dYpkwZJiUlFfjYUqmUbm5uLFeuXJ72iS0uIiMjuXjxYurr6xMATUxMuH79er5//17RoeULmUzGsLAwjh8/Xqh2zem1fPv2bXbs2JHLly8XEplFzZs3b7h//37GxMSQJL28vFinTh22bduW7dq1o6WlJS1a1GYAACAASURBVEny5s2brFevHqOionju3Dl27tyZZ8+epZOTE0uVKkUrKysOGDCAGhoaDA8P5/v371mpUiUePnyYHz9+5IULF1i2bNmvEo5r1qxh06ZNhaph8s9KHUdHRy5ZskTo4RYbG8tevXpRIpEwODiYJOnr60sHBweOGTOGLi4utLS0zFVR9fLlS165ciXX84u+TS6Xs2HDhly2bFmePefTp0/Zu3dvYVt+9epVfvz4kZ8/f+aIESPo4+PD5cuXs0ePHgwNDeW7d+8YFhamkG1/UZaZmckWLVrQxsam2FTUfv78mTVq1OCcOXMUHYpCvH79mjt37qS9vT1btGjBN2/ekPxz+/jhwwe+fv2ac+bMYcuWLenp6UmSefb3Ly6fo/x069YtLlmyhGFhYX97v5z3ct26daxUqRLXrl0rbONyqlQ3bdrEcePGCUnLnH9XrlwpVPeS5J49ezh16lSSpI+PD83MzBgbG8vDhw+za9euQjJ53bp1uZLC4eHhbNiwoTDbh8y+8Nu1a1fWqFGDI0eOLBF9SnP+Fm/evGGDBg1IZlfGz549mx06dGC1atVoZ2dX6I/psrKyeOHCBQ4aNIjly5enuro63dzceOnSpUKTxCyqjhw5QlVVVU6bNq1QbQfnz59PQ0NDRYchEhVLP5IUFnsKi0QFzNLSEsrKyjhz5gz69u1boGOrqKhgx44dqFy5MqytrbF//3789NNPBRpDYaalpYXp06dj2rRpCA4Ohre3NxYuXIjx48ejR48ecHFxga2tbbHp/Xjw4EFs3boVxsbG2L9/P16/fo2BAwfC09MTtra2CAwMLNI9bmvVqoV+/foJPzs7O8PQ0BDXr1+Hqqqq0FOyefPmGD16NAYMGICKFSuiTZs2sLS0hKWlJSIjI/H8+XPs3bsX6enpWLJkCX766Sdoamqid+/eAICWLVuie/fuCAkJQcOGDUESR48exfr16+Ht7Y1y5coJMSgpKeHy5csIDAzEsmXLoKamBpLo168f7OzsEBwcDF1dXQDAhQsX4Obm9s3el0FBQfDw8ICOjg7Wrl2LcuXK4cyZM1i2bBnKly+PFi1aoE2bNjA2Nkbt2rXz820uEiQSCcaOHZun/XtTU1Nx6dIlzJo1CytWrECHDh2E361btw6lSpVCYmIi7t+/j/DwcBgYGKB69ep5Nn5Joaqqij179qBly5bYuXMnhgwZouiQ/rN58+ahbNmymDZtmqJDUYhSpUohPDwc8+bNg6amptAvNj09HUFBQVi2bBkqV64MTU1NHDlyBFpaWgCAlJSUPOkxXJT3awXh5s2bmDhxIrS0tPDgwQMMGDAA1tbW37yvRCJBamoqnj59iq5duyIxMVH4O6moZJ9mpqWlIT09HcrKypDJZFBWVgaQ/d3OWV8jMTERly5dwqlTp3Dt2jU8f/4c79+/x4QJE/Drr7+icePGcHR0xKRJk3Do0CF4enoKMfj6+sLMzCxXL1lDQ0OcP38eSkpK+fU2FTo5n+tatWoJ/YUbNGiA2rVrw8LCAk2aNIG2tjZSU1MVHOm3hYWFwcvLC3v37sXbt2/RvXt3bNu2DXZ2dsXmuFuR9uzZgyFDhmDBggWYPn26osPJ5cSJE+jVq5eiwxCJSq5/yhpTrBQWifJcv379vlptuSDJ5XIuXLiQysrK3Llzp8LiKApyVjYeOHCgULEwePBgXrhwochPP+3evTu3bNlCknz+/Dn19fXp4eHBKVOmsG7dunRwcMjV3qA4S0hI4NGjRxkYGPjN3y9btozu7u4ks3swW1lZCS0ADh8+TCsrK2GK8+bNm2lgYMD9+/eTZK7p0IGBgTQ2Nhb6IcbGxnLkyJEcN24c4+PjheqexMREGhgYsGPHjnRzc+PJkyeFCpl3796xd+/e7Nq1K2fPni3cLpVKGRQUxM2bN3PcuHGsXbs29fT0GB8fz7dv33LMmDEcP348r1y5ktdvX5GRl5/nlJQUmpiY0MrKioMGDRKqbmQyGePj44WxLCws6OXllWfjllTLly+nurr6V71ni5qQkBAqKyvz1KlTig5Fof46w+HIkSP09PSkubk5d+zYkauyc+7cubS3t2f//v0LfE2Gkqh///4cPnw4SXLXrl309PT8ZvuWnH3P8ePH2bt3bwYHB9PNzY3Pnj0T7pOcnMzZs2cL+7wvb3d3dxf6hUulUm7YsIFbt25lYGAgExMTWa9ePeH+79+/54wZM9iqVStOmTIlV5XjlxXCJd2zZ8947Ngxmpub08DAgIsWLeKKFSvo5+cntMooTLNV3r17x9WrV7NFixYEwObNm3PVqlXFrl2Qom3YsIFKSkpcv369okP5yuvXrwmgyPbxFokKO4jtI0SiwunQoUMsV66cMHVcUXIOElavXq3QOIqK5ORk+vj40NramsrKyqxVqxYnT57M4ODgQjUN60fZ29vz3Llzws92dnbCIjFPnjyhgYHBd5OkJUXO3/Xo0aP08/MTbl+1ahU7dOjAoUOHsnPnzpw7dy5Jcvr06bSzs8vV5iHHoUOH2Lp1a2Hhufj4eC5cuJBjxowhSd6/f1/oYRwWFsY+ffrw6NGj/Pnnn9mtWzfhOeVyOe/evcsFCxZw+fLl343dzc1NSEx37tyZa9eu5dixY9mlSxehh6NcLucvv/zCESNG8ODBg0KPXNHfy8jIYEJCAm1tbXnr1i1OmjSJEydOJEm+evWKJ0+epI6ODjt27Mju3bsrONriISsri506dWKXLl2K7BRiuVzOjh070tbWVtGhFBqfP3/m3LlzWbNmTe7atUton5MjOTmZJ06cYEhICP/44w9aWVmVuJ7/BSktLY2jRo3iunXrSJIvXrzgvHnzhIsYfz3WSU9P58yZM3nixAmS5MiRI/nkyRPhO5qVlUVjY2OGh4d/NZaenh6fPHny3VhMTU1z9ZPOyMgost/9ghIREcEpU6Zw3rx5NDY2/mZ7KQ8PDz59+lQB0WVLTU3lvn37hGPp2rVrc+rUqeJC2Pnk119/pbKyMnft2qXoUL5pw4YNrFWrlvjdFonyiZgUFokKqeTkZJYtW1ZYWVmR9u7dSxUVFc6ePbtIJjYVJTY2luvWrWObNm0IgIaGhlyyZAmjoqIUHdoPe/jwIXv06EF3d3cOHDiQmpqawu+io6OppqZWpF5PQUpKSqKfnx+nT58uLHJ37NgxVqhQgerq6vTw8OCSJUuEE97Vq1fTysqK58+fF57j8uXLbNq0KatUqUI3Nzd26dKFZmZmlMvlzMrKylX95O7uzhEjRgg/v337lu7u7kIF6pff3YSEBG7cuFHomXz79m127txZ+P3w4cO5dOlSZmZmsk+fPnRycuKIESPYuHHjEl+9+HfCwsK+6q/5888/c+/evZTJZHRxcWGNGjU4YcIEkqSfnx+vXbumiFCLrZcvX7JcuXJF9kLm7t27WaZMmW8myEoqmUzGESNG/NAMhvv377NTp07/WC3+ZdW+mGj432RlZXH06NHcsWMHyewE3m+//fbd71xcXBw1NDS4Z88eTpkyhbVr12aVKlW4du1a4SKjnp5ersU1c/4+tWrV4uXLl4XbcvZ9RX0WlqLlLCbn5+cnvNdSqVToyTx48GDhImZBkclkvHTpEocMGUJ1dXWWK1eOgwYNKhaz7goruVzO6dOnU1VVlYcPH1Z0ON9lYWGRayFJkUiUt34kKSz2FBaJFKBcuXKwtrbG4cOHFd7T19nZGRUrVkSfPn3w6dMnrF27tkT1YPu3NDU1MWbMGIwZMwbPnj3D3r17sXXrVkybNg1dunSBi4sLevfujUqVKik61O8yNDTEli1bcOrUKdSsWRPt2rVDly5dYGFhgfDwcOjr66Nu3bqKDrNQKl++PKysrGBlZSXcZm9vjxcvXiAkJAR3797F1atXUb16daSkpGDNmjWIiopClSpVEBwcDFNTU5iamuLOnTt4+PAhoqKiMG/ePLx//x7r16+HsbExdHV1oaqqChUVFWRkZAh9GAEgMjISJHP1qJXL5VBSUsLFixcREBCAFStWAABev36NtLQ0AEBUVBRUVVVRqlQpyGQydOzYEaNGjULp0qWxaNEiLF26FDY2NiBZIvpukkRmZiZKly79t/fr168foqOjERkZCRcXFyxduhQAIJPJkJiYCCUlJTx+/Bjly5cX+hZ/+dkQ5Q0dHR2sWrUKP//8M3r06IHGjRsrOqQf9vnzZ0yZMgXTp0+Htra2osMpNJSUlLBlyxYAyNVvNi4uDtWqVUN0dDTWr1+Phg0b4uXLl6hdu/Y/9heVSCRISEhAxYoVc90uk8mgpKRUIrZt/5aysjLU1dWRmpoKmUyGsmXLokKFCoiKikJ6ejrKlCmT6/4PHz5E48aNsXPnTrRv3x7t27dHixYt4O7uDlVVVQQFBaFOnTp4//49dHR0hP1UamoqDh48iKZNmwL4sx/ul/s50b+T87n/ch+U098ZANzd3TF8+PACieXJkyfw8vKCt7c3oqOjYWFhgU2bNsHe3j7XeguivCWXy/Hzzz9jx44dOHnyJHr06KHokL4pISEBAQEBmDRpkqJDEYlKNDEpLBIpSN++fTF8+PBvHmQXtJ49e8Lf3x82Njb49OkTdu7cCVVVVYXGVJQ0atQIv/zyC+bNm4ebN2/C29sbU6dOxejRo2Fra4sBAwbAysrqHxNPBSkuLg5nz55FQkICBg8ejHLlyiErKwtlypTB/v37oaWlhT179ig6zCJHQ0MD5ubmMDc3x9SpU4Xb79+/j0ePHuH27du4c+cOPn/+DD09PVSsWBGtWrVCq1atMHfuXAQEBKBx48Zo164dxo0bBycnJyHBPG/ePOH5YmNjAUBICsvlcigrKyMhIQGnT59G8+bNYWhoCCA7kaampgYXFxdkZGSgTJkycHZ2RpkyZYSEcHp6OuRyOapVqwYAxTopTBIxMTEICAjA3bt3kZiYiJCQEEyePBl9+vT5KimxZs0axMbG4tq1a3j58iU6d+6MDh06wM7ODo6OjvD29oa+vj66du2KBQsWoH///rh69So6duyooFdYvA0fPhzHjx/HwIEDcf369VzJjsJszpw5UFdXz7VAlii3nO/e3r17cfXqVWzYsAF16tTB9evXERAQgCFDhsDR0RGqqqpYuHAhkpOTUa5cOcyePTvX87x69QoNGzZEixYt0KVLF7i6usLQ0DDXd/vLBHRJEx8fj5CQEISEhEBFRQXu7u65vketWrXCtWvX8PHjR2hoaCA5ORkAkJWV9dVzde7cGYGBgcLP27dvx+vXr5GUlIQqVaoIn3l9fX0AEIoO1NTUci3OKco7qampuHfvHqKiohAbG4umTZtCIpEgPT0dZmZm0NfXx9u3bxEVFYV69erl+fgfPnzA/v374eXlhTt37sDIyAjjxo2Ds7OzsKikKP9kZWVh2LBhOHr0KM6ePQtTU1NFh/RdZ86cQZkyZdClSxdFhyISlWhF40haJCqGrK2tIZVK4e/vDzs7O0WHg06dOiEgIACWlpZwcHDAwYMHc63kLPpnEokE7dq1Q7t27bB69Wr4+/vDy8sLTk5OKFu2LBwdHeHq6or27dsrNOEWHx+PsWPH4uPHjzAwMICnpydmzpwJPz8/ZGVl4eDBg8KK4KK8UblyZXTo0OGbJ8E5CVhHR0chyTt79mxMnToVO3fuRGZmJmxsbHJVesTFxUFNTS1XpTCQvbq0uro6HBwcAAAZGRlYvnw5unfvDl1dXZw7dw6mpqbQ1NTMni70/4kAHx8fBAUFCdVDxTUhDADHjx9HQEAA1NXVUa9ePZQuXRrGxsZYuXIlkpKSMGzYsFz3b9CgAbp16wa5XI4GDRrA0dER8fHxePToEaRSKY4fP46xY8di1qxZALLfy5zkuijvSSQSbN++HYaGhvj111+/SggWRsHBwdiwYQN8fX0VfhG4KPj48SMqV64MJSUlZGVlwdjYGM2bN4ebmxsA4Pr162jQoAGysrJw9+5dofoUyN6eamlpYf78+fDx8cGTJ0/Qrl07KCkpoUePHhgyZAgsLS1LREJYLpfj5cuXCA4OFpLAwcHBiI6OhpKSEho3bozu3bt/dWHFzMwMhw4dgre3NyZMmICLFy+iYcOGKF++PMLDw1GjRg2hWjtnX5GZmYlSpUrBxsYGysrKqFKlCgBAT08Penp6BfvCSzg1NTWMGzcOqqqq6NatG/bv34/Q0FC0adMGCxYsgLa2NlRVVXH27FmMGDEiT8ZMT0/HqVOn4OXlhTNnzkBDQwMDBgzA9u3b0axZszwZQ/TPMjIyMGDAAFy6dAl//PEHWrVqpeiQ/taJEycKXdGMSFQSiUlhkUhB1NXV0aNHDxw+fLhQJIUBoHnz5rh69SrMzc1hZWWFkydPisnBfynn5MjGxgaJiYk4evQovL290alTJ2hpacHFxQUDBgxQyPTnixcvolSpUjhx4gTevn2LQYMGwc7ODrq6uqhQoQKCg4OxadOmYp0YLExy3ucvk1vW1taoVasW7t27h4oVK6J37965HvPy5UtIpVKhVUFOQmTLli1YvXo1dHV1AQA3btxAUlIShg4dCk1NTXTu3BkDBgxAy5Yt0aRJEygrK8Pf3x+bNm2Cu7s7bGxscsVU3IwfPx4BAQHw9PREx44dc1VJKSkpISws7KvHVK9eHbGxsfDy8oK2tjbOnTsHILuC2MPDAwcPHkTnzp0BIFe1tSj/1KpVCxs3boSrqyt69uyJFi1aKDqk75LL5fDw8ICdnZ3YUuQHaWlpYdGiRejWrRvCw8Px+vVrNG/eXPh9TpsCIHsmRHbLvGwSiQRyuRxjx46FkpKSMGMjKCgIu3fvxsiRIxEVFYXly5cXqynLKSkpePjwoZD4DQkJwYMHD5CSkoIKFSrAyMgIRkZG6NWrF4yNjWFgYPDdC/9VqlSBtbU19u7di1u3bkEqlcLd3R1Xr16Ft7c3Zs2a9VULj1KlSgEAatSoke+vVfTP7t69C4lEAplMhoyMDKipqUEqlSI2Nhbq6uoIDAyEjo7OfxpDLpfj2rVr8PLywsGDByGVSuHg4IDTp0+jW7duJeLCS2GSmpqK3r17Izg4GJcvXxZmixVWmZmZ8PPzw6ZNmxQdikgk+qemwxQXmhOJ8o23tzcrVqzI9PR0RYeSS1RUFPX09NiiRQu+f/9e0eEUK9HR0Vy+fDmNjIwIgK1ateKaNWv47t27Aovh9u3bHDZsmPC3XbJkCWfPnk2SjIyMpL29vbDIjKhw+OsikKGhobx69Wqu265cucLy5cvnuu3hw4fU0NAQFrw7d+4cGzVqxLi4OJLk+fPnqa2tzW3btgmL0HzL5s2befLkSb548aLILtz0+vVrOjo68vr167luj4mJ4c6dO2liYpJrpXuZTMbw8HBKpVKePHmSHh4erFSpEtetW0eSDAoKYsOGDenn50eS4mI5CuDo6MgmTZowLS1N0aF81++//86yZcv+4+Jootx27tzJnj17cuTIkdyyZQtJCtueu3fv0s/Pj1euXOHNmze/+927fft2rgU7c0RHRxfZRVTlcjmjo6Pp6+vLRYsWsW/fvmzUqBElEgkBsH79+uzVqxfnzp3Lo0ePMjw8/F8vIvzy5UsuW7bsq32NqGiQSqXCdyZnIT8yezHaoKCgf/28T58+5axZs1i/fn1KJBKam5tz9+7dTEpKypO4Rf+7hIQEdurUiVpaWnz+/Lmiw/kh586do4qKCj99+qToUESiYg0/sNCchF9cXf8eiUTCH7mfSCT63yQkJEBTUxPHjh2DtbW1osPJ5cOHD+jRowdSUlLg7++fL33HSrqHDx9i79692Lt3L2JiYmBhYQFXV9cCWYDDxcUFDRs2xKxZs5CamgplZWVhTGtra/Tu3RtDhw7N1xhEeSs5ORlPnjxBq1athJYUGRkZcHd3x40bN1CrVi18/PgRvXr1wrx583DixAn88ssvWLVq1d/2c8vKykL79u0RGhqKtLQ0lC1bFo0bN4aBgQEMDAzQpEkTGBgYQFtbu1AvUhkTE4OuXbti1apVqFevHuLj4/H27VtERERAJpPB3NwcJiYmuR6TlZUFR0dHHDp0CMrKyvD09MTIkSPRoEEDANnTrAcNGoTBgwcr4BWJ4uPjYWhoCBcXFyxfvlzR4Xzl06dPaNSoEcaPH4+ZM2cqOpwiJzk5GeXLlwcATJ06FXZ2dujQoQPOnDmDnj17om/fvti9e3eulhw5bQxytoFftpYoajIzM/HkyROh8jfn3/j4eJQuXRqGhoYwMjKCsbExjIyM0KxZs0K9uK1IMXK+EzmOHTuGjRs3olmzZnBycvqhFgPx8fFCn+Bbt27BwMAAAwcOhLOzM+rUqZOf4Yv+QXx8PHr06IHExERcuHChyCwQPXr0aDx79gznz59XdCgiUbEmkUhA8m+ngIpJYZFIwWxtbaGhoYHff/9d0aF8JTExEba2toiIiMD58+fFvnD5RC6XIzAwEN7e3jh06BBkMhns7e3h4uICc3PzfFtIKTo6OtfBfGBgIPbs2YOQkBCcOXNGnAZfRPEvi8QlJibi8uXLePDgAerVqwdXV1e8fPkSrVu3xufPnzF27FjUqFEDZmZmXyVFvySTyRAZGYlHjx4hLCxM+Pfx48dfJYtzEsUGBgaoX79+oZlGunv3bqxatQqVK1dGmzZtULp0aWhpacHExOS7Uy23bduGgwcP4vz58/Dw8EBCQgIcHBxw48YNbN++HTdv3lRIGxhRttOnT8POzg4BAQHo1KmTosPJZfTo0Th//jwePnwo9kz8F3K2ZSTx008/YdasWUICq3///ti4cSMqV66ca3u3fPlyWFpaolmzZoiOjsahQ4ewf/9+VKlSBe7u7ujZsyckEkmhSxR//PgxV9/fkJAQhIWFQSqVQlNTU0j85iSB9fT0iswiiyLF2b59OyIjIzFz5kyUKVMGWVlZUFVVRVZWFjZt2oT79+9/9/wjIyMDvr6+8PLygp+fH6pUqQJnZ2e4urrC2Ni42LaZKkrevn0LCwsLqKiowN/fH5qamooO6YeQRN26dTFt2jSMGTNG0eGIRMWamBQWiYqA3bt3Y8KECYiNjYWqqqqiw/lKWloaHB0dcevWLZw9e7ZQ924sDtLS0nD69Gl4e3vDz88PlStXhpOTE1xcXNCyZcv/fBB+4sQJqKmpoVOnTrmqq+RyOR49eoRVq1Zh3rx5YmV4MZZTOffhwwc8efIE169fx40bN1C7dm1s2LDhq6TyP5HJZHj16lWuRPGXyeIyZcpAX18/V6K4SZMm0NbWVkiyOCMjA6mpqYiMjES9evVQtWrVf3yMk5MTlJWVsWfPHri6uiIxMRExMTHYsWMHjI2NCyBq0d8ZPnw4Ll68iJCQEKirqys6HADZPT3btGkDPz8/WFpaKjqcIk0qlcLOzg6GhoZYuHAhDh06hDFjxuDVq1eoWLGicD+SGDJkCOLj43Hy5ElMmTIF9+7dg7OzM+Lj43HmzBksWLAAHTt2VNhryVn87a8J4NevX0NJSQl6enq5qn+NjY3FPr2if+XTp09wdXXFgAED4OTkhE+fPmHKlCnYtm0bkpKSsG3bNjx//hwbN27MtVDjrVu3sGvXLhw8eBDp6emwt7eHq6urkHwUFQ6RkZEwNzeHpqYmTp8+LawxURQEBQWhdevWePXqlXi+IRLlMzEpLBIVAZ8+fRJ26N27d1d0ON8klUrh5uaGU6dO4dSpUzA1NVV0SCVCfHy8sAL4tWvXoKenJyxQp62t/a+ec+XKlVi2bBn09fVRp04dmJiYYOjQoZg5cybc3NzQtGnTPH4VopJKLpcjMjLyh5PFX7ahyK9kcUJCAmbPno3ffvst1+0/kgg3MzPDhAkTYG1tjYyMDJAUpraLFCspKQnNmjVD9+7dsWXLFkWHA7lcjvbt26NOnTo4fPiwosMpFuLi4mBra4vatWsjPDwc8+bN++4ivYMHDxYudK1YsQItWrSATCbDb7/9Bj8/P5w+fTrXdPr8kpKSgtDQ0FztHx4+fIjk5GSoq6vnqvw1MjKCoaHhdxd/E4n+Vx8+fIC+vj7i4uIAAG/evIGhoSG0tLTQs2dPBAUFYfr06bnaR2VlZcHZ2RlxcXFwdXVF7969xQWnC6GnT5/C3NwcjRo1wokTJ4rcscjs2bPh5+eHu3fvKjoUkajYE5PCIlERYWVlhbp162Lr1q2KDuW75HI5xo0bhx07duDw4cPo2bOnokMqUcLDw+Hj4wMvLy88e/YMHTp0gIuLC/r27ftDlY453r9/j2HDhkFDQwNOTk7w9fXFkydPcOnSJQwdOhT9+vVDp06dCt3UWlHxkZMs/jJR/OjRIzx+/BipqakoU6bMd3sW50WyeOvWrejXr1+uCsMf4ebmBhMTE4wcOfI/xyDKe5cvX4aZmRlOnz4NKysrhcayfft2/Pzzz3j8+LFYBZWH5HI5EhISIJPJvtneKGcWRHp6OoYOHYrLly9j1apVcHR0BABcvXoVY8eOxf379/M0LpKIiYnJVfkbHByM58+fgyS0tLRyVf4aGRmhfv364n5WlO+6desGQ0NDdOnSBf7+/qhduzYsLS1x//591KxZEz179sz1OZTJZEhPT8/3dS1E/15ISAgsLCxgYmKCgwcP5pr1V1Q0a9YMffr0wZw5cxQdikhU7IlJYZGoiNixYwemTZuGt2/fFuqpWSQxd+5c/Prrr9izZw+cnJwUHVKJQxJ3796Ft7c39u3bh0+fPsHa2houLi6wsbH5oYPD2NhYODs7o1u3bpgxYwauXLkCFxcXjBs3DiEhIdizZ08BvBKRKDe5XC60ofhrz+K/Jou/bEXxvyaLpVLpV616SEIul3/zeWQyGV6+fInx48fDxsYGHh4e//m1ivLHxIkTsX//foSGhqJKlSoKiSE+Ph56enqYPHkypk2bppAYirsfWTzu+fPn8PDwwJs3b+Ds7AxlZWWE3IFrPAAAIABJREFUhISgefPmmDp16r8eWyqV4vHjx7kSwCEhIYiLi0Pp0qVhYGCQKwEsLv4mUqSHDx/izJkzuHnzJtTV1eHp6QkDAwMAQFRUFCIiItC5c2cFRyn6UTdu3IC1tTWsrKywe/fuQtl28J9ERERAR0cHwcHBMDIyUnQ4IlGxJyaFRaIiIi4uDjVq1IC/vz+6du2q6HD+0Zo1azBp0iSsX78e7u7uig6nxMrKysKFCxfg7e2NY8eOQVVVFX369IGLiwtMTU2/edKckZGB0qVLIyIiAj///DMWLFiAAwcOID4+Hlu2bEF6enqRrDoQFV9fJov/2obiy2Txl4liKyurfz09PCUlBXK5HGpqakKSOD09HXPnzsXSpUvz8qWJ8lhaWhpatmwJY2Nj+Pj4KCSGUaNGISAgAA8ePCiQFgWiPyUnJ+Pdu3do2LAhgOxFmI4dO4bIyEhERkaib9++sLW1/eF93JeLv+UkgR89eoTMzExoaGjA2Ng41wJwenp6RTJJIypZsrKyoKKigqNHj+Lw4cPQ0NDA6NGj0ahRI0WHJvobFy9eRK9eveDs7IxNmzYVmgV8/1dr1qzBmjVrEBERIS5WKBIVADEpLBIVIRYWFtDV1cXGjRsVHcoP2bVrF4YNG4ZffvkFM2bMEHfsCpacnIzjx4/D29sb58+fx7Vr19C2bdvv/l1I4sCBA7h8+TJOnDgBHx8fdOnS5YcqsESiwuBbyeJHjx7h0aNHmD59OmbOnPlD2yWpVIr09HTs378fd+7cQbVq1ZCQkIAqVapgwYIFBfBKRHkpKCgIJiYm8PHxEdoGFJQ7d+6gbdu28Pf3h7m5eYGOXdIdPHgQ9+7dQ2BgIMqVKwdDQ0MhaWtgYPC3s7BIQiqVwtfXF/fv3xcSwH9d/O3L9g81atQQj3tERcbMmTOhoqKCSZMm5eoR/O7dO2zbtg3JycniRc9C7NSpU+jbty9Gjx6NFStWFOltT5cuXWBsbIw1a9YoOhSRqEQQk8IiURGydetWzJkzB2/evCkyV3+PHz+Ofv36YezYsVi+fHmRPkgpTmJjY6GhoZEruZuRkYELFy4gNDQUPXv2hKGhIQBgw4YN2L59e573WBSJFEUul+P9+/eoUaPG397v1atX2LVrF0aNGoXk5GRMnToVjx8/xvbt23Hjxg3Mnz8fnz9/LqCoRXlp3rx5WLduHUJDQ1GzZs0CGVMmk8HExAQ6Ojo4cOBAgYwp+lPdunWxd+9emJqa4vXr17h+/Tru3LmDK1euoFatWli5ciV0dHS++3ipVIpLly5hyZIlaNKkiZAANjAwgJqaWgG+EpEobz1//hz/x96dx9WY//8ff7QzjGVQdtJIsoSsyRLKli0pcophbGPfhsFgLGMZ+zI+mWHoJJGlSFJZM/YlErJkTUlpaNFyzrl+f/jqx2Amo7pa3vfbrZub0znneh7Ocp3X9b5er/Hjx7NgwQKaNGnCjRs3mDVrFlu3bqVkyZLMnj0btVrNwoUL5Y4qfIC3tzeurq78+OOP/PjjjwX6u1ZCQgKGhoaEhIRgY2MjdxxBKBJEUVgQCpC4uDgqVarEkSNHClR/rzenMzk7O+Pu7p6veyIXZVOmTCEqKor4+Hhu377NzJkzadSoEdHR0ejr69OnTx+5IwpCnpEkidmzZ3P37l02bNiQNXSuevXqhIaGUqNGDSwtLVmyZIlY8VkAZWZm0qpVKypWrMj+/fvz5Eu0u7s7kydP5ubNm1StWjXXtyf8fy9evKBnz55MnDgRe3v7d/ZDUlJSGDRoEDY2NowePfof70eSpAJdcBGKpszMTO7evUutWrU+2LLm2rVr2Nvbc//+fQAuXbpEq1atsLW1pXr16pw7d46tW7dm9RoW8o/ffvuNkSNHsmzZMiZOnCh3nM/m4eHBhAkTiIuLE98XBSGPZKcoLM4RFoR8wtDQkHbt2rFr1y65o3ySjh07cuTIkaxVw+np6XJHEj5g586drFu3jhMnTnDw4EG2bNnCjh07OHDgANOnT+enn35CrVbLHVMQ8oSWlhZeXl5MmzaN0qVLo1KpAKhUqRJHjx4FoE+fPhw5cuSDt5ckCXGwPP/S09PDw8ODkJAQNm/enOvbi4+P54cffmDu3LmiIJzHJEmidOnSDBkyhKVLl+Ln58ezZ89Qq9Wo1WpKlCiBg4PDR3tMv/1aFgVhoaCQJInz588zbtw4qlSpgrOz80d7mNevX5+yZcuycOFC/P39mTFjBhs3bmTOnDlUr16dNWvWUK9ePTQaTR4/CuGfrFixgpEjR/K///2vUBSEAfz8/OjevbsoCAtCPiNWCgtCPvLrr7+yYMECHj9+XOD6ukZERGBnZ0fdunXx9fWlZMmSckcS/k9sbCwODg4sX76cevXqoVKpsLOz48KFC8DrgvGSJUs4ePAghoaGMqcVhLwxevRo4uPjmTRpEi1atGD9+vWEhISwZs0aqlWr9tE2FBkZGTx79gw7Ozt0dXWzhtu9GXRnYmJSYFoAFXYrVqxgzpw5XL16FWNj41zbzrBhwzh16hRhYWFi0JhMJEli3bp1+Pj4UKxYMVq1aoWpqSn+/v68ePGCtm3bMn36dLljCsJnefDgAdu2bcPDw4PIyEhat26Nm5sb/fr1o2zZsh+9XUREBL6+vlnzI+bOnUuFChXyMLmQXZIkMW/ePBYsWIBSqaR///5yR8oRr169onz58nh4eNC3b1+54whCkSHaRwhCARMTE0OVKlUIDQ2ldevWcsf5ZPfu3cPW1pby5csTEBDAV199JXekIu/N6bCBgYEEBQVRoUIFdu3ahY6ODufOnQNetwBxdHQkMTFR5rSCkHcePHjAqlWrOHr0KDdu3KBSpUosWrSIAQMGkJaWxo0bN2jcuPF7t5MkicePHxMeHv7ekLuUlBQMDAyoU6fOO4XievXqUatWLbE6Jo9pNBpsbGzQ0tLiyJEjuXKw9cyZM1hZWXH48GHRIzEHaTQa7t27R1hYGFeuXEFPT49p06ahq6v7zv9jcnIys2bNyhpalJaWxuXLlzlx4gTPnz+nWrVqmJub0759+wJ3sF0QAF6+fMmuXbvw8PDg+PHjmJiY4ObmhkKh+Mc+2X/3sUHCixcvRk9Pj+7du/P111+LzykZSZLElClTWL9+PT4+PvTo0UPuSDnG398fR0dH4uPjxcIhQchDoigsCAVQ27ZtsbS0ZOXKlXJH+U9iYmLo3LkzGo2GoKAgKleuLHckgddDkE6ePElkZCRNmjRh//79FC9enAoVKrBlyxZMTU3ZtGnTB2+bkJBAuXLl8jixIOSN8+fPY2hoSI0aNbIui4qK4o8//iAiIgInJ6dsrdTRaDQ8evSIiIiIbBeLzc3NMTExEV/Cc9G9e/do2LAh8+bNy/FTcNVqNc2aNcPMzOyj7QmEf5eamsq1a9eyCsBhYWFcvXqV5ORkSpYsiYWFBRYWFnTu3JkePXq80+bhjz/+YNu2bYSEhJCUlERUVBRBQUE0aNAAGxsbDAwM/nX7opewkN+oVCqCgoJQKpX4+vpSvHhx+vfvj6urKy1btvxPz9e/P88lSeLly5e4u7uzdetWtLS0WLFiBXZ2djn5UIRsUqvVjBw5ku3bt7Nv3z46dOggd6QcNWzYMJ48ecKBAwfkjiIIRYooCgtCAbRmzRp++eUXHjx4UGBXtSQmJtK9e3diY2MJDg7GxMRE7khFmkqlIiQkhMzMTFq0aIGhoSFPnz5lzpw5XLt2DScnJ/r06UO1atXeu+2tW7cwNzenS5cuKBQKevbsKSaxC4XGixcvePXq1TstIlQqFbq6usTGxrJ//35mzpxJXFzcf97G28XiN4Xit4vF+vr6mJmZvVMoftOGQhSLc8Zvv/3G2LFjuXz5MnXr1s2x+/3111+ZPn06N2/eFAdAs0GSJGJiYrIKv1euXOHKlSvcunULjUZD9erVadSoERYWFll/Ghsb/+O+ULdu3XBycmLw4MHMmzeP48ePY2hoyJkzZ0hPT2fDhg306tXrHzOJgrCQH0iSxOXLl1EqlXh5eWXtS7u5udGtW7dsHeD4NxqNhqioKMLDw7l582bWsGFdXV2qV69OkyZNsLKyyoFHI3yKzMxM3NzcCAwM5ODBg7Rs2VLuSDlKrVZTuXJl5s+fz/Dhw+WOIwhFiigKC0IB9PjxY6pVq8aZM2do0aKF3HH+s5SUFBwcHLh69WrWqh0h7x05coSNGzdibGxMXFwcZ86cQU9PD3t7e4YMGUKtWrU+ekohvP4CceTIETw9Pdm9ezcAffv2RaFQYGNjI3qnCgXajRs3sk6ZnTFjBrVr16ZKlSrvXKdChQocO3YsxyezvykW/71QfP36dZKTk9HX189aWSyKxZ9HkiTs7e2Ji4vj1KlTOdL3Ny4ujjp16jB79uxCMwQoJ2VmZnLz5s33CsDPnj1DX1+fevXqZRV+3/z8U0/UjylVqhQLFy5k7Nix1KpVC3d3d2xtbQGYMWMGMTExrF69mlKlSuX0QxSEHPH48eOsPsHXr1+nZcuWuLm54eTklKNnaT19+pTly5eTnp5ORkYGpUuXpkaNGpibm2Nubi56DMskLS0NJycnzpw5Q3BwMBYWFnJHynGnTp3C2tqaJ0+evDenQRCE3CWKwoJQQFlZWdG6dWt++eUXuaN8loyMDBQKBSEhIQQEBBS6I98FgYODA9bW1kyaNCnrsrCwMLy8vDh27BjTp0/HwcEhW/eVmprKvn378PT0JDAwEENDQ1xcXFAoFFhYWIjVVkK+9U+rAStUqMCgQYM4ceIEJUqUwNDQEHt7e8qXL8+qVatIT09n586deTaE8e/F4rf//Hux+O2exaJY/M9iYmKoX78+48aNY86cOZ99f0OGDOH8+fNcunSpyA+XS0xMzCr6vikCR0REkJGRQfny5WnUqNE7BWAzM7Mc+TdTqVT4+flx8eJFMjIyOHjwIBEREajVanR0dEhMTKRFixaEhoZiZGSUA49UEHJGUlISe/bswcPDg6NHj1KzZk1cXV1RKBTUrl0717Y7d+5cqlWrRoMGDahTpw6lS5fOtW0J/y45OZlevXpx69YtQkJCqFOnjtyRcsW0adM4ceIEp0+fljuKIBQ5oigsCAXUihUrWLt2LVFRUQW+0KZWqxk1ahTbtm3D19c3awWPkDcmTZrEs2fP2LhxI8WLF3/nd7///jt79uxhw4YN7/RTzY64uDh27tyJp6cnZ8+epV69eigUClxcXKhevXpOPgRB+CwZGRno6el99L108ODB1KtXj6lTp3L06FFOnTrF5cuXuXPnDh07dmT48OH54ouaJEkfbUPxdrH47UKxubm5GBz0lh07dqBQKDhz5gyWlpb/+X7+/PNP2rRpw7Fjx2jbtm0OJszf3gx/e3v1b1hYGA8fPkRLSwtTU9P32j9UqlQpV/Zj3hzokSSJqKgovL292bt3L9OnT8fR0ZHnz5/z559/MmLECJ48eZLj2xeET6VSqTh8+DAeHh7s3bsXAwMDnJyccHV1pXXr1nmyv5+ZmfnOAZnU1FSuXr1KZGQkJUqUQF9fn7Zt21KmTJlcz1LUJSYm0q1bN549e8bhw4c/eT+8IDEzM2Pw4MFMnz5d7iiCUOSIorAgFFAPHjygZs2aXLhw4bO+uOYXkiQxffp0Vq5cyfbt2+nbt6/ckYqM+Ph4BgwYQKlSpRgxYkTWABGNRsO5c+cYOXIk+/bt+6xC7u3bt9m2bRuenp7cvXuXdu3aoVAocHR0FF8sBFmlpqbi6OiIl5fXR5+LAQEBjB07lrt372ZdplarycjIwNvbGyMjIywtLSlfvny+bJeSnWKxqanpe20oimqxuH///oSHh3Px4kWKFSv2ybdXqVQ0bdqUBg0aoFQqcyFh/vBm+Nvf2z+8Gf7WsGHDdwrA9evXz/N+82lpaRw7doykpCRsbGz46quvSEpKIj09nYEDBwIwZsyYj/YUVqvVAPnydS0UHleuXEGpVLJt2zbi4+Pp1q0brq6u2Nvb/6f3oJxy/vx5/Pz8SEhIQFtbmydPnnD16lXq1avHH3/8IQYM56K4uDjs7OxQqVQEBwdTqVIluSPlmps3b1K3bl2uX7+eoz39BUHIHlEUFoQCrHnz5nTs2JFFixbJHSXHLFmyhBkzZrBx40aGDh0qd5xC781Kqjt37rB27VoCAgJ48eIF9evXp379+pw5c4Z+/foxderUHNve2bNn8fT0xNvbm6SkJHr06IFCoaBr1645MiRFELIrKSkJe3t7Hjx4wJUrV/7xNNlJkyaxYMGCrKJWVFQUU6ZMyXrOPnv2jIEDB/LNN9/kSfac8KZY/KGexUlJSejp6X20Z3FhboeQkJBAgwYNcHFxYdmyZZ98+7Vr1zJr1iwiIyMLRW9ESZKIjY19Z+Xv34e/vb3y18LCglq1ask+CPfhw4dMmjSJv/76CyMjI5KTk5kxYwYtWrRAo9GQkZFBSkrKRwtbkiTx6tUr+vTpw/r16/n666/z+BEIhdmTJ0/w8vLCw8OD8PBwmjdvjqurK87Ozvmid6+Hhwe7d+/G0tKSFi1aUKNGDYyNjTEwMMDOzo6uXbuKXum55NGjR3Tq1IlSpUoRGBhY6IvvS5YsYdOmTURGRhb4s18FoSASRWFBKMB++eUX3N3duX37dqH6EN24cSOjRo1iyZIlTJkyRe44RU5sbCzBwcFkZGTQvn17TExMcmU7mZmZHDp0CE9PT/z8/ChevDhOTk4oFAqsrKxkLygIhVtiYiJdunQhMTGRw4cPU61atX+8fkpKCsWLF896Xs6aNYugoCD8/PwwMDDgxo0buLq6EhUVlRfxc5UkSTx+/PidQvGbP/9eLP57z+LCUiwOCAigR48eHD169JPaPzx9+hRTU1Pmz5/PuHHjcjFh7sjMzCQyMvKdlb9hYWHvDH97uwDcsGFDvvrqK7ljf9C0adN4/vw5K1asIC4ujnnz5rFnzx7WrVvHoEGDsnUfaWlp9OnTh8uXL3Po0KFCOeBJyDspKSns3bsXDw+PrM8dhUKBq6trvmhB9EZSUhJOTk707NmTUaNGvfO7+Ph45syZg5OTE+3atZMpYeF1584dOnXqRI0aNdi/f3+RGIBpZWWFtbU1S5culTuKIBRJoigsCAVYVFQUJiYmXL58mUaNGskdJ0ft2LEDV1dXpkyZwsKFCwtV0Ts/kiQJjUaDtra2LP/WL1++ZM+ePXh6enLkyBFq1KjBwIEDUSgUmJmZ5XkeoXB7c1qmWq0mODg426s5U1JS+OKLL9DS0mLevHmUKlWKCRMmAK9bBrRp04Z9+/bli1VeueHtYvHfVxe/XSz+UM/iglgsHj58OMHBwVy9epUvv/ySq4//wv9qDBcePOdOXDIZag36Otp8bViSpjW+oodFZZbNGEdYWBgXL17M9603/vrrr3cKv1euXOHatWvvDH97s/K3UaNGOTb8La988803dO3aFScnJwBCQkKYMWMGGRkZzJ07l969e2d97v2TjIwM3NzcCAwM5MCBA7Ru3Tov4guFhFqt5ujRo3h4eLBnzx50dHTo168frq6utGnTJl8eAA8MDGTx4sXs37+fL7/8koSEBB4+fMjVq1c5c+YMOjo6LFu2TNbWFoXRtWvXsLW1pVGjRuzevTvP2+3I4enTp1SqVInQ0FDx3ioIMhFFYUEo4CwtLenatSsLFiyQO0qOCwwMxMHBgUGDBrFu3TrR0y8PZeeLcm6Jjo5m+/bteHp6cuXKFSwtLVEoFPTv379QnIotyCs6OppOnTpRokQJDh069EmnZbZq1YrSpUvTsmVLtm/fTpkyZRgyZAgGBga4u7vTrFkzVqxYke+LgTntQ8XiN3++KRZ/rGdxfi4yJiUlYWFhgWUXJ5LNunEjJgk9HS0y1e/v7765PONpFAt6N2BIz/Z5H/gj3h7+9nYB+MGDB1nD395e/duoUaNcG/6Wlw4cOMDo0aOZOXMmDRs2xMfHh8TERKpUqcKRI0f46aef6NixY7buS61WM2bMGLZu3cqePXvo0qVLLqcXCrrw8PCsPsFPnz6lS5cuuLq60rNnz/eG+uY3L1684JtvviE+Pp66detSvXp1UlNT0dPTo1WrVnTu3FnuiIXO+fPn6dKlCzY2Nnh5eaGvry93pDzx+++/M2PGDGJiYsT3PEGQiSgKC0IBt2jRIrZs2cLNmzcL/Be4D/nzzz/p3r07Xbt2ZevWrUVmJ0l4LTw8nG3btrFt2zaePHmCra0tCoWC3r17U7JkSbnjCQXM/fv36dixI5UqVeLAgQP/2EP4QyIiIkhMTMTb2xu1Ws2ZM2fIzMzEwsKCunXrMmnSpCKxsie73hSL/76q+EPF4rdXF+eXYrFKrWHM7yEcvJeBtpYW2drLlTSgpc1Qa2N+6GKGrk7eHlx79eoV165de6f/79WrV0lKSqJEiRLvrPy1sLCgfv36lChRIk8z5iU/Pz/27NnDo0ePiImJwc/PD1NTU3bu3MkPP/zArVu3sl2IkCSJWbNmsXTpUjw9PXF2ds7l9EJBExsbi5eXF0qlkrCwMCwtLXF1dWXAgAEYGhrKHe+T/PXXX3h5eVG6dGlevXpF7dq1sbKyIiUlhYCAAB4/fszAgQOpUqWK3FELvBMnTmBvb0/fvn357bffitSBZXt7e4yMjNi0aZPcUQShyBJFYUEo4G7dukWdOnUIDw+nfv36csfJFVeuXMHOzg5LS0t27dolii657M3wOZVKlW92TDUaDSdOnMDT0xMfHx9UKhV9+vRBoVDQqVOnfJNTyL9u3bpFx44dMTMzw9fX95MLYW9eF29LS0tDT0+PS5cuUaFCBUqUKEHx4sXFAYt/IUkS0dHRH+xZ/PLlyw8Wi83Nzaldu3aeFYsPHz6C8o42Jx+motJ8+v6trrYWHcwM+dWlSa4Uht8Mf3t75W9YWFjW8Ldq1aq91/4hPwx/yyvBwcGYm5tTpUoVHj9+jI6ODmXKlHlnheaDBw+oUaPGJ9/38uXL+f777/n1118ZMWJETsYWCqDU1FR8fX1RKpUEBQVRuXLlrD7B5ubmcsf7bLdu3eLKlSvo6+ujo6PDiBEjsLa2xtzcHH9/f86ePVtk3ldyQ2BgIH369OHbb79l9erVRerfMjk5mfLly+Pj40OPHj3kjiMIRZYoCgtCIWBhYUGfPn2YO3eu3FFyze3bt7G1taVatWrs37+fMmXKyB2pwIuJiSElJYW4uDi0tLSoVq0aVatWzfr9li1bGDBgAAYGBjKmfF9aWhr+/v54enoSEBBA2bJlGTBgAAqFAktLy0K5Yl74POHh4dja2tKsWTN8fHxyrA/ikydPCAoKIiIiguPHjxMZGUnv3r1xcXERp9f+B28Xi/++uvjtYvHfexb/vVh848YNzM3NUalU6OjocP36db7++msePXpEUlJS1vX09PSoV6/eB7N83aQ1MS8zKO8455PeUxIOrqV4LUu+qGOFrrYWg61qMqv7hwtDQ4YMwcvLK1vPx5SUFLp06ULdunWzisBxcXFZj+HtArCFhUW+Hf6WV1xdXbl8+TJNmzalcePGtG7dmqZNm7J06VKGDx/+2fsQmzdvZvjw4cyfP5/p06eLz50iRqPRcOzYMZRKJbt27QLA0dERNzc32rVrV2gKexcuXGDQoEGYm5ujr69PVFQUw4YNY8iQIQC0bNmSH374gV69esmctGDatWsXLi4uTJ06lQULFhS595E9e/bg6upKfHx8vm+pIgiFWXaKwmL5lSDkc46OjuzYsaNQF4Vr167Nn3/+ia2tLTY2NgQGBmJkZCR3rALL09OTkJAQHjx4QNu2bZEkCbVaTcWKFenVqxflypXjzJkzDB48WO6o7ylWrBiOjo44OjqSkJCAj48Pnp6erF69mjp16qBQKBg4cCDGxsZyRxXygQsXLtC5c2c6duyIp6dnjrSgSUtLY/369ezYsYPatWvTpEkT1q5dS+3atVEqlYwZM4bbt2/nQPqiRUtLi6pVq1K1atV3iupvisVvF4oPHTrEihUrePnyJbq6upiamlK3bl3Mzc0xNDRER0eH9PR01Go19vb2jBs3Dn9/f27fvk3ZsmV58uQJzZo1Y9++fVkrvvX09NDS0uL8/edktBmNxm8p6uTn6H5ZLisHahWSRoWWngFaWu8WflJvnyUl4ihfWtoDoNJIbDp5jy71KtK05vtFWj09PcaMGcOyZctIT09HW1ublJQUrl69yuXLl7l06RJXr17lxo0bqFQqDh8+zKtXr7CwsMDV1RULCwvMzMxEW6UPGDNmDK6urhQvXhxJkpg7dy6vXr3i6NGj1KtXjy5dunxW/8ohQ4ZQpkwZBgwYwPPnz1m6dGmRK+gURdevX8/qExwdHY2dnR3u7u707t27UJ7FtmPHDho1asTvv/9O8eLFGT9+PMnJySQmJvL48WNKly79zoE2Ifu2bt3K0KFDWbhwIdOmTZM7jiz8/Pyws7MTBWFBKADESmFByOferIq6fv06devWlTtOroqPj6dbt2789ddfBAcH/6dTPwWoXLkyfn5+NGvWjOjoaGJiYnj06BHnzp3jwoULrFq16qMr6PKrqKgovLy88PT0JDIyktatW6NQKOjXr98nDRMTCo+TJ0/SrVs3HBwc+P3333Oszcjx48eZNWsWS5YswcrK6p3f3bx5k+nTp7Nt27ZC3as1P/h7sXjfvn2cPXuW9PR0NBoNALq6upQvX57hw4ezf/9+nJ2dmThxIj169GDAgAFUrVoVW1vbT9525REb0StbOevvqpfxxGwZj6TKgLeKxVoA6kxKflGMc+fOUadOnazsgwYN4sWLFzRq1AilUsm9e/fe207jxo1ZsGABW7ZswdzcvFAf/M1pR48eZeHChcyePZu2bduyYMEClEolFhYWlCxZks2bN3/2NkJCQujduzfOzs64u7uLVkaFUFxcHNu3b0epVHLx4kUaNWr65mZIAAAgAElEQVSU1Se4UqVKcsfLVVOmTOH27dv4+fkB8Ntvv7Fs2TJq1KhBzZo1KVmyJMuXLxcHRD7RunXrGD9+PGvXruW7776TO44sVCoVRkZGLF++PF8uQBGEokS0jxCEQqJevXoMGDCAWbNmyR0l1yUlJdGrVy9u375NUFBQoS+E57SMjAy++eYbrKysGD169Du/S05O5ocffgBe900siCvQJEni4sWLeHp64u3tzfPnz+nWrRsKhQJ7e/scax0g5G8hISH06tWLQYMGsW7duhw7nVeSJL755hsaNGjA5MmTsy5PSUkhMjKSFStW4ODggIODQ45sT/h09+7do3bt2ixatIglS5bQtWtX7t69y/nz57NaSkiShL29PQ0aNKBGjRqYm5tjZmbG7fhXOG08Q+az+8Qqp1J1rBItvf97z5AkJHUmkkaNzhel0dJ+vdJU9SKOp96zMKhqTunW/dErUzEriyopnqee05g0ZQq1K5bJav1w5coVkpKS0NXVpVmzZjRo0IBGjRrRpEmTrOFvKpUKjUaDvr4+gwcP5uuvvy4Sn/GfS5IkVCoVenp6eHp6EhQUhIeHB87OzrRu3Zpx48bx7NkzKlSokCPbO3v2LN26daN9+/Z4eXnlu5ZLwqd79eoV+/btQ6lUZp2ZNnDgQFxdXWnQoIHc8fJMREQE7du3z2qLERAQwLhx43jx4gV3797F2tqamjVryh2zQFm0aBE//vgjf/zxB66urnLHkc2xY8fo2LEjT58+pXz58nLHEYQiTRSFBaGQmDNnDn5+foSFhckdJU+kpaXRv39/Tp48SWBgIE2bNpU7UoFy8OBBRo4cSd++fenYsSN16tShWrVqGBgYsHv3bubNm8eVK1fkjvnZ3pxy7enpyZ49e9DV1aVfv34oFAratm1baPr+Ce/y9/fH0dGRsWPH5spp3YMHD0ZLS4tmzZphZWXF3bt3OX78OImJifTp00cUhGUQHR2NiYkJJUuWRF9fn8TERAC+/PJLtLW1KVasGI0aNaJDhw4kJCSwfft22rVrl9Wz+MWLF+jq6lKz90RUJtakxz0g5o9xVJvkg7b+x09tTXt8g2e75lGivg2lmvYixmMSJeq1p2y7waiS4nm2ez5fWvZAk57Kl3dDsoa+WVhY4O/vT7ly5Vi0aBFaWlrvDdGTJInMzEy0tbX59ttvRVH4P0hNTWXs2LGUK1cOpVLJpUuXcmV1Z0REBHZ2dtStWxdfX18xbLIA0mg0hIaGolQq8fHxQa1W07dvX1xdXbGxsfmsdiMFmZ+fHz4+Pty+fRsHBwemTp0q9p3+A0mSmDFjBsuXL8fb27vI7ydMnDiRS5cucfz4cbmjCEKRJ4rCglBIhIeH07BhQ27dukXt2rXljpMnVCoVQ4cOZc+ePezfv5/27dvLHalAuX79Olu2bOHly5eUKlWKV69ecfbsWYyMjOjRowfDhw+XO2KOSk5OxtfXF09PT4KDg6lcuTIDBw5EoVBQv359ueMJOcTHxwcXFxdmzZrF7Nmzc+W01kePHrFlyxZu3ryJRqNBV1eXPn36ZPUpffDgAcWKFaNy5cridHIZPH/+HCsrK4YOHcrUqVOzLu/duzeOjo74+/tjYWGRdVaEJEm0a9eO0NDQf73vUs0dKNthSNbfJUki40kkBlXMAFCn/EX8gZWoX8ShyXhF2Q5DKVG3DRaVS+I3tt079zVhwgR0dXV59uwZu3fvRqPRkJGRQcmSJXnx4gUlSpQgIyODlStXcv78eVEUzobExESCg4MxMDCgffv2lC5dGoD+/fsTFRXFuXPnkCQpV94X7t27h62tLeXLl+fAgQOibVEBERkZiVKpxNPTk0ePHtGpUydcXV3p06ePaAH0f1JTU9/rmZxbr6PCSKPRMHbsWP744w/27t1b5AfRSpKEiYkJY8aMYdKkSXLHEYQiTxSFBaGQkCSJunXrMnjwYKZPny53nDyj0WiYOHEi7u7u7Ny5k549e8odqUCRJIlnz55x/fr1rIFNVatWpWHDhnJHy1WxsbF4e3uzbds2Lly4gIWFBQqFggEDBlClShW54wn/0ZvBLYsXL2bKlCl5ss2HDx9SvXp1wsPDWb58Oc+ePaNx48YEBgbi5ubGuHHj8iSH8NrTp0/p0qULlStXxt/f/52ixZuicKlSpRg+fDinTp2iVq1aAPTo0YNatWoRUsKGpHTVB9tHxO2ah0FVc8q2/fgpv+kxt0g8/DtaOnp81WUMemVfr0otXVyPK7Pt3rnum6LwsmXLANi0aRO+vr7s37+fMmXKcO3aNapWrQog2kdkU+/evXn06BFxcXGoVCoWLFjA0KFDkSSJuLg4jIyMyMzMfG9Vdk6JjY2lc+fOqFQqgoKCxOdJPhUfH4+3tzdKpZJz587RoEED3NzccHFxoXLlyv9+B0WURqMRq4Q/kUqlYsiQIfj5+XHgwAGsra3ljiS7NwuZ7ty5g4mJidxxBKHIy05RWCxxEYQCQEtLC0dHR3x8fIpUUVhbW5tVq1ZRrlw5HBwcinyPrk+lpaWFoaEhhoaGckfJUxUrVmTChAlMmDCBmzdvsm3bNtavX8/3339Phw4dUCgUODg4UKpUKbmjCtn066+/Mnbs2Dwd3LJmzRru3LnDihUrePXqFenp6ejq6jJ16lS++OILvLy8RFE4Dx0+fBhXV1cyMjLIzMzM6v0ZERFBUlJS1vV69uzJiRMnGDZsGIcPHwZAR0cHPT091LoGaEt6aOm+7gurpVcsq32ElpY2WlrvF0Q0acmk3jpD8tVg0h9HvL6uQQlitkzIuo4W0NzfnHPnzn00f0REhOiR/xkePXqEv78/KpUKgF27dvHbb79Rt25drKysMDIyYunSpQwcODDXirUVK1bk+PHjdO/eHWtra4KDg/n6669zZVvCp0lLS8Pf3x+lUklAQADly5dn4MCBbNy4EQsLC7njFQiiIPxp0tPTcXFx4fjx4xw5cgRLS0u5I+ULvr6+1K9fXxSEBaEAEe/+glBAODo6cunSJaKiouSOkqe0tLSYPXs2K1asYPDgwaxdu1buSAWKRqORO4KszMzMmD9/Pnfv3uXEiRPUrl2bSZMmYWRkRP/+/fH39yczM1PumMI/WLZsGePGjWPz5s15VhCOjY0lICAAe3t7dHV1ad68OatXr+bs2bOULl2aGTNm8PDhQxISEvIkT1F39+5d+vTpg6GhIQqFgmvXrmX9AO+tDJ09ezahoaHvfV7q62R/t1f9KolYz2k8WqMg+WowmfEPMBrwMzWm+1N94o53fip3HZlVrPyQV69e4e3tTY8ePd65PDw8nFu3bmU7U1EWFRVFo0aNePDgAWq1mj59+tCqVausNiHJycn89NNPub56t0yZMgQFBWFmZoa1tTVXr17N1e0JHydJEidPnmTEiBFUqlQJNzc3vvzyS/z9/Xn06BHLli0TBeFPIPaFsi81NZVevXpx+vRpTpw4IQrCb/Hz86NXr15yxxAE4ROIorAgFBAWFhaYmJiwe/duuaPIYty4cWzZsoWJEycyb948REub7Ll48SIvXryQO4bstLW1sba2ZsOGDcTExODt7Y1arcbR0ZFKlSoxZswYTp8+LZ5X+YgkScydO5cffvgBLy8vBg0alGfbLlasGKGhodjZ/f+WAImJiVSoUIEbN24A0KtXLy5dupRnmYoyExMTnjx5wuDBg9m8eTM1a9bM+gHQ19d/5/ra2toYGBiQnJz8zuVfG/7TgLB3X/s6xb/kyybdqPTNKioqlqBd7MuP3tKolMFHV9mpVCqGDRuGpaUlbdq0AUBXV5dVq1YxatSoInX2z3+l0Who164d1tbWHD58GB0dHXR0dJg7dy6lS5dm8uTJ+Pn50bZt2zzJU6JECfz8/Gjfvj1t27blzz//zJPtCq/dvn2b2bNnY2JiQtu2bbl79y6rVq3i6dOneHp60rlzZ9Hv/T+IjIzExMTkvfdN4V0vXrygc+fOREZGcvLkSczNzeWOlG88fvyYixcviqKwIBQwoigsCAXEmxYSu3btkjuKbFxdXdmzZw8///wzEydOLPKrYLPj22+/Faur/8bAwIBevXrh4+NDbGwsS5YsISIiAisrK2rXrs2cOXO4ffu23DGLNEmS+P7771m8eDF79+7FyckpT7dfpkwZbGxsUCgU+Pv74+/vz7Bhw3BwcMhqAbB27VpsbW3F+1AeKVnydUF3yJAh3L9/P+sHXj9f3hzQ+f7776levTq2trZZQybVajUATWt8hZ7Ou23VXt0P43nQ/0iPvYPOl+8ODyth3g79CjX/MZeejhY1y70/sOrN8+LKlSvo6+uzffv2rN8tWrSIhIQEGjduzK+//prNf4Gi603BffTo0RgbG6NSqbL+T7du3crFixdxdXWlX79+eZZJX1+fbdu2MWDAAGxtbQkMDMyzbRdFCQkJbNiwgVatWmFqasru3bsZMWIEDx8+JCQkhEGDBvHllx8/cCP8OxMTE2JjYzl06JDcUfKt+Ph4OnbsSHx8PKGhoVl964XX9u3bR+XKlcXKaUEoYMSgOUEoQC5cuECzZs24f/8+NWrUkDuObI4dO0bPnj1xcHDg999/FytCPiI6OpqqVaty4cIFsYOWDQ8fPmT79u0olUoiIiJo3rw5CoUCZ2fnIteXWU5vJnlv2bIFPz8/OnXqJEuOyMhIdu3ahVKpRK1WM2jQIEaMGEGJEiUIDQ1Fo9FgZWVFqVKlxJT2PPLLL78wb948SpcunXVZdHQ0aWlp9O7dm4EDB9KsWTO0tLQwNTXNus7o0aMxNTXF0rYPCmU46bF3iN0ygWoTd6JKiuf5oV8pbtyYUi0d0dLW+eC2ozcMRZ36Aj7wewMtDWZmdbh8+fI72/yUgu+cOXOYO3dutq9flAQFBXHp0qWPrqres2cP06ZN4/z585QpUyZPs0mSxKxZs/jll19QKpU4Ozvn6fYLs/T0dAICAvDw8ODAgQOULVsWFxcXXF1dady4sXjfzQV9+/alRIkSeHh4yB0l33ny5Am2trbo6+tz6NAhsV/4AXZ2dnz99dfiYKcg5CPZGTQnVgoLQgFiaWlJjRo12LNnj9xRZNW+fXuOHDmCv78/jo6OpKWlyR0pX3qz09q4cWO5oxQI1atXZ9q0aYSHhxMWFkbbtm1ZvHgxlStXpnv37mzfvp3U1FS5YxZqbyZ5K5VKDh06JFtBGKBOnTrMnDmTa9eucf78edq3b0+FChXw9vYmICCAU6dO4eTkxN69e2XLWNSkp6czdOhQHj9+nPUDkJGRQUZGBvD6/+3tgjC8HhpoYGBArzaN0forGlSvrytpVOiXr07FgYspbeX80YIwgKTKwNBx9ju9hKuN98J46EqmTJn83udQeno6kydPzlrF/E8/gwYNEp9j/2Djxo1Zq4WfPn3K9u3bGTp0KFu2bCE9PR0HBwc2bdqU5wVheP1la+HChSxatAgXFxfc3d3zPENhIkkSp0+fZtSoUVSqVAkXFxeKFSuGr68v0dHRrFy5kiZNmoiCcC7p1avXOwMdhdfu3btHmzZtKF26NEePHhUF4Q948eIFx44dE60jBKEAEiuFBaGAmTJlCqdPnxY97IAbN25gZ2dH7dq18fPzE6cO/k2/fv0oXry4WPHxGdRqNceOHcPT0zOrdYuDgwOurq7Y2Nigo/PxIpLwaTIyMlAoFBw+fJhDhw7RtGlTuSMBsH79elauXEnTpk3R19cnNDSUzZs307p1a7y9vdm4cSMnT56UO2aRkJaWhiRJFC9ePNu3CQ0NZezYsdy9e5fZs2dj3WcQLpvO86l7tZr0VLT0DN4pHGsB9WODOLB1LUOHDmXx4sWUK/e6BUVqaira2toUK1bsE7ck/F21atUICwujXLlydO3alS+//JIKFSoQGBhI8eLF2bNnz3sHAuSwefNmhg8fzoIFC5g2bZooXH6CqKgolEolnp6e3Llzh/bt2+Pq6krfvn3fOTNAyF0JCQkYGRkRHByMjY2N3HHyhZs3b9KpUyfMzMzw9fXNamUkvMvb25vhw4fz7NkzDAwM5I4jCML/ESuFBaEQcnR05NSpU0RHR8sdRXZ169bl5MmTPH78mA4dOhAfHy93pHxDpVIRHBxMly5d5I5SoOno6NCxY0f++OMPnj59yu+//87z58/p2rUr1apVY/LkyVy+fFkMqPtMaWlp9O3bl+PHj3Ps2LF8UxC+c+cOu3fvZubMmXh7ezNw4EDq1q2LjY0N+vr6NGnSBAMDg6z+pkLuKlasWLYLwo8fP8bFxYV27dphYWHBrVu3mDp1Kq2+NmSItTG62p9WsNM2+OKdgrCuthZDrY3Z/8dqQkJCOHnyJHXq1GHTpk1oNBq++OILURDOAeHh4cTExHDjxg2uX7/O5cuX8fDwYP369dy9e5dGjRqhVCrzxcrGIUOGsHPnTubMmcP3338vPhf+RWJiIu7u7lhbW2NiYoK3tzfffPMN9+/f5+jRowwZMkQUhPNYuXLlsLa2xs/PT+4o+cLly5dp06YNTZs2xd/fXxSE/4Gfnx9du3YVBWFBKIBEUVgQCpjmzZtTtWrVIt9C4o0aNWoQGhqKSqWibdu2WacTF3Vnzpzh5cuX2NnZyR2l0Pjiiy9wdnZm//79PHnyhJkzZ3Lq1CmaNGlC/fr1WbRoEQ8ePJA7ZoGTkpKCvb09YWFhhIaG0qBBA7kjZSlZsiSXLl2iQ4cOAHTu3Jk7d+4wdOhQ3NzcaNmyJc7OzmLFeD6SlpbGzz//TJ06dbh16xZ//vknW7dupVKlSlnX+aGLGR3MDD+5MPyGrrYWHc0Mmd7FDIAOHTpw5coVpkyZwtixY7G2tiYsLCxHHk9RZ2xszObNmwkICGDWrFk0b96cYsWKZbXbcHV1JTg4ON/MFnBwcODAgQNs2LCBYcOGiQNGf5ORkcG+fftwdHSkYsWKzJo1iyZNmnD+/HmuX7/OjBkzivTMjPygV69e+Pn5FfmDGqdOncLGxgY7Ozt8fHzEQb5/kJGRQUBAgGgdIQgFlCgKC0IBo62tTd++fbNOZRfAyMiIo0ePZq1wuH37ttyRZBcYGEizZs0oX7683FEKpQoVKjB69GhOnz7N7du3cXJyYvPmzdSsWZN27drx22+/kZiYKHfMfO/Fixd07tyZe/fuERoami9OAX9bxYoVqVixIu7u7hw9epS5c+fSv39/WrZsiZGREQcOHGD48OFyxxR43Yt037591KtXj1WrVrFmzRrOnTtHq1at3ruuro42v7o0YZBVTQCyWxvW1nrdMmKwVU3WuzRBV+f/70br6+szffp0bty4gZGREZaWlkyYMIGXL1/mwKMrukqWLImbmxuurq6Ympry8uVLIiIigNfDQf38/DA2NpY55bs6derE4cOH2bNnD87OzqSnp8sdSVaSJHHu3DnGjBlD5cqVcXJyQltbm927d/PkyRPWrFlD06ZNRbuNfKJXr17cv3+f8PBwuaPIJiQkBFtbW5ydnfHw8EBPT0/uSPnasWPHSE1NpVu3bnJHEQThPxA9hQWhADp58iRt27blyZMnVKxYUe44+UZqaiqOjo5cunSJQ4cOYWFhIXck2VhaWtKjRw8xzT4Pvfni6+npibe3Ny9fvsTe3h6FQkG3bt3EKXV/k5CQQOfOnUlJSSEkJIQqVarIHemDQkJC+Pnnn3n27BnW1tZ8991376xmliRJFDNkdvPmTSZMmEBISAjjxo1j9uzZ2R46dv7+c+bsi+B6zEv0dLTIVL+/v/vmcvNKpZjXsx5Na371r/fr7+/P2LFjSUtLY8WKFfTv3188Tz6RSqXiwYMHhISEULNmTTp37oxKpUJXV5dHjx7RpUsX2rdvz+TJk6lVq5bccd8TERGBnZ0d5ubm7N27t8iden7//n08PT1RKpXcunWLNm3a4ObmhqOjoyxDAYXsa9CgAU5OTvz4449yR8lzfn5+ODk5MW7cOJYuXSret7Nh9OjR3Lp1i+DgYLmjCILwN9npKSyKwoJQAGk0GqpWrcqPP/7IqFGj5I6Tr2RkZODm5kZgYCAHDhygdevWckfKc0+fPqVixYqcPn2ali1byh2nSMrMzCQoKAhPT098fX0pVqwYTk5OKBQKWrdujbZ20T5RJzY2FltbW3R0dAgKCsr3k7xTUlJITk4mNTX1nVWJ8+fPx8jIiI4dO2JiYiJjwqLp5cuXzJs3j9WrV9O+fXtWr16Nubn5f7qv8OgX+F99woUHiVy68wRtPX1KFi/G14YlaVqjLPYNK9Ogyqf1N3316hWLFi1iyZIlWFtbs379eszMzP5TvqJo69atbN68mVq1aqFSqbCzs6Nr166sXr0aCwsLunfvjo6ODvr6+nJH/ah79+5ha2tL+fLlCQgI4Kuv/v2AQkH24sULfHx8UCqVnDhxgtq1a+Pq6opCoch3K7qFj/vxxx85ePAgFy5ckDtKnvLy8sLNzY05c+Ywa9YsURDOBkmSqFatGtOnT2fMmDFyxxEE4W+yUxRGkqR//Xl9NUEQ8pMxY8ZINjY2csfIl1QqlTRy5EipePHi0sGDB+WOk+e2bt0qffXVV5JKpZI7iiBJ0osXL6QtW7ZInTp1krS0tKQaNWpIM2bMkK5fvy53NFk8fPhQMjU1lZo3by49f/5c7jj/yZvXVmhoqNS3b1/JxMREcnd3lzlV0aFWq6U//vhDMjIykmrWrCnt3btX0mg0OXb/VlZW0oIFC3Ls/iIjIyVbW1tJT09P+uGHH6Tk5OQcu+/CrH79+tLevXul1NRUaefOnZKpqak0btw4acaMGVKTJk2kpUuXyh0xW2JiYqSGDRtK9erVk6Kjo+WOk+MyMjKk/fv3S05OTlKxYsWkr776Svruu++kM2fO5OjrUsg758+flwDp4cOHckfJM+7u7pK2tra0cuVKuaMUKG+eKw8ePJA7iiAIH/B/tdx/rPcW7aVKglCAOTo6cvz4ceLi4uSOku/o6Ojw66+/MnHiRHr27MmOHTvkjpSnAgMDsbOzE8Ov8olSpUoxaNAggoODefToEWPHjiUgIABzc3MsLS1ZuXIlMTExcsfME3fv3qVNmzZUrFiR4OBgypYtK3ekT5KamsrDhw85ceIEixYtYu3atcTHx2Nvby9Oh84jb/oEf/fdd4wZM4br16/Tu3fvHF3R9aZFQU4xNTXl0KFDbNu2ja1bt2Jubi4GOf2LzMxMqlWrRosWLShevDj9+vWjYsWKDBkyhIULFzJx4kS2bt3K9evX5Y76rypWrMixY8coXbo0rVu35s6dO3JH+mySJHHhwgXGjx9PlSpV6Nu3LyqVCm9vb2JiYli/fj0tWrQQKy0LKEtLS6pUqcK+ffvkjpInli1bxqhRo9i4cSMTJkyQO06B4ufnR5MmTahevbrcUQRB+I9EUVgQCihra2vKly+Pr6+v3FHyJS0tLRYuXMjPP/+Mi4sLGzdulDtSnlCr1Rw6dIiuXbvKHUX4gCpVqjB58mQuX75MeHg4dnZ2rFy5kqpVq9K5c2eUSiXJyclyx8wVN27coG3bttSpU4eDBw9SqlQpuSNlm1qt5uDBg6xYsYLx48fz008/ERUVhaOjI76+vqxatQonJye5YxZqT58+ZciQIbRs2RJjY2Nu3rzJrFmzKF68eI5vS6VS5fhBNS0tLfr168fNmzfp168fffv2pWfPnty7dy9Ht1NY6OnpMWjQINq3b8/AgQNxc3Pjr7/+ypoV0L59e+7fv19gChFly5YlKCgIMzMzrK2tuXr1qtyR/pOHDx+yaNEizM3NadasGRcuXGD+/PnExMSwe/duevXqla/beQjZo6WlRc+ePfHz85M7Sq6SJIk5c+bwww8/4OXlxdChQ+WOVOA8ffqUPn36yB1DEITPIHoKC0IBNmrUKO7evUtQUJDcUfK1TZs2MXz4cH7++WemTZsmd5xcdfbsWVq2bElsbCxGRkZyxxGyQaPREBoaiqenJz4+PmRmZtK7d28UCgW2trY5umJRLmFhYdjZ2WFlZcWOHTsK5NA9Nzc3dHR06NmzJ7a2tkVuaJRcMjIyWLduHT/99BM1atRg7dq1tGvXLle32ahRIwYPHpyrK8bCw8P57rvvuHDhAjNnzmTq1KkF8nWR2x4+fEhAQADJyck8fPiQ58+fY2VlxZUrVwgPD+fUqVNyR/wkb+YeHDp0iAMHDmBlZSV3pH/18uVLdu/ejVKp5NixYxgbG+Pm5oZCoRC91AuxQ4cO0aNHD549e0bp0p/WT70gkCSJSZMmsWHDBnbv3k337t3ljlTgvH36uTg7URDyJzFoThAKucOHD9OjRw9iY2ML1Ko7OezatQsXFxcmTpzI4sWLC+0pjXPnzmX//v1cvHhR7ijCf5CWlsaBAwfw9PTkwIEDlC1blv79+6NQKGjatGmBfN6ePXuWLl260LVrV7Zu3Yqenp7ckf4TtVr90S89t27d4uzZs7Rp04aaNWvmbbBCLCgoiPHjx/P06VMWLFjA8OHD8+QgSf369Rk5cmSuD82RJAkPDw+mTp1KmTJlWL9+Pba2trm6zYIsPj4ed3d3jh8/Trly5Zg8eTJNmzaVO9YnU6vVjB49Gg8PD/bs2UOXLl3kjvQelUpFcHAwSqUSX19fDAwMcHZ2xs3NjVatWhXIzyLh06Snp1OhQgU2btxI//795Y6To9RqNSNGjGDHjh3s27cPGxsbuSMJgiDkClEUFoRCTqPRkJaWRrFixdDWFt1g/k1wcDC9e/fGxcWF//3vf4XyqHbLli3p2LEjCxculDuK8JmeP3+Oj48Pnp6enDx5ElNTUxQKBQMHDqRWrVpyx8uWEydO0L17d5ydnXF3dy8Ur7nU1FTOnj2Lnp4eZcuWZerUqSQkJGBsbMyrV68YN24cHTt2lDtmgRYVFcWkSZPYv38/I0aMYP78+ZQrVy7Ptm9mZsb48eMZNWpUnmwvMTGRmTNn8r///Y9+/TY9/N4AACAASURBVPqxYsUKqlSpkifbzo/u3r3LpUuX6Ny583sHvOPj49HT0yvwKxclSWLmzJksW7YMpVKJs7Oz3JGQJImwsDCUSiVeXl48f/6cbt264ebmRvfu3cVK9iKof//+aGlpsX37drmj5Ji3V+sHBgbSokULuSMJgiDkGlEUFgRB+JvTp0/TrVs3bG1tUSqVhepLTnx8PIaGhpw4cQJra2u54wg56N69e3h5eaFUKomMjMTKygqFQoGTk1OeFss+xaFDh+jTpw/ffvstq1atKhQHrlQqFTNmzODSpUukpaVRtWpV0tPT2bFjB/r6+qxcuZLTp0+zc+dOuaMWSCkpKSxatIhly5bRvHlz1qxZQ6NGjfI8R+3atfn+++8ZNmxYnm73/PnzjBo1isjISH766SfGjh1bYFfWf46zZ8/St29fjI2NqVKlCu3ataNTp07Url1b7mg5btmyZUybNo0NGzYwfPhwWTJER0ezbds2PDw8iIiIoEWLFri5ueHs7JxvP1+EvLF9+3ZGjhzJs2fPCkWv6FevXtGvXz/Onz9PcHAwDRs2lDuSIAhCrspOUbjgf0MTBEH4BK1ateLEiROEhobSs2dPUlJS5I6UY4KDgylVqhQtW7aUO4qQw4yNjZk5cyY3btzgwoULNG/enJ9++omKFSvSq1cvfHx8ePXqldwxs/j6+tKzZ0/Gjx/P6tWrC0VBGODMmTMEBQUxbtw4Tp48yahRo8jMzERfX5/ExERSUlIKxWrovCZJEt7e3tSpU4ctW7awZcsWjh8/LktBGF4X/+Xo5d2sWTPOnj3L0qVLmTdvHpaWlpw8eTLPc8itRYsWtG3bNqsYfObMGUaMGIG9vT3z58/n+PHjwOvnTUE3ZcoUNm7cyKhRo1i8eHGePabk5GQ8PDzo1KkT1apVY8OGDTg4OBAZGcmZM2f47rvvREFYoGvXrqSmpnLs2DG5o3y2pKQkunfvztWrVwkNDRUFYUEQhP9TOL6lCYIgfIIGDRpw8uRJbt++ja2tLYmJiXJHyhEHDx4sNIPJhA/T0tLC0tKSlStX8vjxY/z9/SlVqhSDBw+mYsWKDB06lKNHj6LRaGTLuH37dhwdHZk9ezaLFi0qVL0nS5QoQXR0dNZAGlNTU6Kjo2nVqhVLlizh/PnzTJ48WeaUBUtYWBjt2rVj0KBBDB48mMjIyKxTluWiUqlkK+7r6OhkrRZu3Lgxbdu25ZtvviEuLk6WPHL58ccfuXfvHoMGDWLFihWsXLmSAQMGkJiYyLRp04iMjCw07y1Dhw5l586dzJkzh2nTpuVaYVitVhMUFISrqytGRkaMGzeOWrVqcfz4ce7evcu8efMwNTXNlW0LBVOZMmVo3749fn5+ckf5LM+fP8fW1pZHjx5lteMSPo1arS4UB+IEQXifaB8hCIWQSqUiPT2dEiVKyB0lX3vy5Al2dnZoa2sTFBRExYoV5Y70n2k0GipVqsTPP//M0KFD5Y4j5LHk5GT8/Pzw9PQkKCiIypUr4+LigkKhoEGDBnmWY9OmTQwfPpzly5czYcKEPNtuXjI2NqZjx46MHj2akydPoqOjg7m5OTdu3MDMzEwMrMmmhIQEfvzxR9zd3enRowfLly/HxMRE7lgAVK5cmWXLluHi4iJ3FE6cOMF3331HdHQ0ixYtYtiwYUVmNfrfV2yrVCpSUlJISEgoMH3VP0VISAi9e/emf//+OdqD/erVqyiVSrZt28azZ8/o2rUrrq6u9OjRg2LFiuXINoTCa926dSxZsoSHDx8WyAMxT58+xc7ODo1GQ3BwcIHe188v1Go12traBfL5IAhFjegpLAhFiCRJ3Lt3j+DgYCIiIkhISODs2bNMmTKFIUOGFIpeYLkhISGBbt26kZCQQHBwMMbGxnJH+k8uXrxI06ZNefz4cZEeUCRAbGwsO3bswNPTkwsXLtCwYUMUCgUDBgygatWqubbdNWvWMHHiRFl7Y+aF/fv34+Pjw4ULFzAyMmLjxo3Url2blJQU7ty5w19//YW+vj6tWrWSO2q+pFKp2LhxI7NmzcLQ0JDVq1fTuXNnuWO9w9DQkHXr1uHk5CR3FAAyMzNZs2YNc+bMwdzcnA0bNmBpaSl3rFwRFxeHj48PtWrVomvXrnLHyXNnz56la9eudOjQgW3btv3nuQcxMTF4eXn9P/buPK7GNP7/+KuFyBLZQrbsxFgGSUmU0qKIEco6SgzZGVt2NcbYszMqlCxZSpSt7EOIUCg7UVkqWs/9+8NXv6/vGMOo7pbr+Xj4w+k+9/U+LWf53Nf1ufDy8iIyMpJ27drh6OiIvb09VapUyeXUQlH28OFD6tSpw6VLlwrd887Dhw8xMTGhQoUKBAcHo6mpKXekQmngwIGUKFGCwYMH/+3C98eVaUWlTZggFDWiKCwIxYifnx9nzpyhUqVKVKtWjXLlylG6dGmWLFmCvb09rq6uckcssFJSUrC1teXWrVscPXqU5s2byx3pmy1cuBA/Pz8iIyPljiIUILdv32b79u34+Pjw4MEDjI2NcXBwoHfv3mhoaOTaOO7u7sycOZM///wTBweHXDtvQZaQkIC6ujrq6ur8+uuv7Nu3j/bt21OvXj3c3d0JCQmhc+fOcscsUE6dOsXYsWOJi4tjzpw5/PLLLwXygqWmpiYbN27Ezs5O7iifePz4MePHj2fPnj24uLiwYMECKlasKHesXPPq1Sv69u2LsrIy9+7do3LlyuzevZuIiAiys7PR19cvFrP8bty4Qffu3dHV1WXv3r2ULVv2q+6XmppKQEAAXl5ehIaGoq2tjYODA46OjjRp0iSPUwtFWZs2bbCysmLevHlyR/lqd+7cwcTEhHr16nHw4EHKlSsnd6RC69ixY5iamqKhoUFqaiqdO3dm+PDhsrd6EgTh34mN5gShmBg7diyLFy+mc+fOODk5MXLkSAYOHEjv3r0ZOnQo8fHxckcs0MqWLUtgYCB6enp07tyZixcvyh3pmx0+fLhYzqoSvqxJkybMnz+f2NhYwsPDadSoERMnTkRLS4t+/fpx8OBBMjIy/vP5JUli5syZzJ49m127dhWbgvDr16/p27cvy5YtA2DQoEE0b96cunXrMnfuXEaPHs327dtlTllwPHr0CHt7e4yNjWnbti0xMTFMmDChQBaE4cPS2ILYm11bWxt/f38OHz7M0aNHadKkCV5eXkWmz+Pu3bspUaIER48e5d69e/zwww9Mnz6dDRs2sH37dvr06cOjR4/kjpnndHV1OXPmDLGxsZiYmJCUlPSPx2ZnZ3Ps2DEGDx6MlpYWLi4uaGtrc+zYMeLi4li4cKEoCAvfzdbWtlD1Fb5+/TqGhoY0b96cw4cPi4LwdzIyMmLu3LlcvXqVuLg4DAwMcHNzo0SJEnTo0IHVq1fLHVEQhO8gisKCUMg9fvyY+Ph41q9fT58+fahevToAcXFxeHp6sm7dOoYOHSpzyoJPTU0NPz8/bGxs6Nq1K8eOHZM70ld79eoV586dw9zcXO4oQgGlpKREp06dWLt2Lc+fP8fX1xeFQkHfvn2pUaMGo0eP5ty5c99UXJIkiQkTJrB06VICAgLo3bt3Hj6CguXkyZNoaGgwY8YMFAoFTZs2pV+/fhw5cgSAn3/+uVA9h+SVtLQ0FixYQOPGjYmLi+PcuXNs2bKlwM/2/L+9bAsaMzMzrl+/zujRo3FycqJLly7cuHFD7ljf7fHjx9SuXZu0tDTgQxG8fv36BAYGsmzZMlRUVAgICJA5Zf6oV68e4eHhvHv3js6dO/P06dNPvn7jxg2mTp1KnTp1MDMzIyEhgY0bN/L8+XM2b95Mly5dxHJuIdfY2NgQGRlJXFyc3FH+1cWLFzEyMqJz584EBARQunRpuSMVeqqqqpibm3P37l1q1qzJnDlziImJISEhATs7OyIiIuSOKAjCdxDvFgShkFNTU+PatWs8fvyYv/76i5CQkJwNRZKTk9m+fTsNGzaUO2ahoKqqyubNm3F2dsbCwoJ9+/bJHemrhIaGoq6uTqdOneSOIhQCJUuWxMbGBn9/f54/f46Hhwc3b95EX1+fBg0a4ObmRkxMzBfPkZ2djbOzMxs3biQoKAgLC4t8Sl8wqKurc/36deD/99F78uQJ9evXJy0tjWbNmmFra8vLly/ljCkbSZIICAigWbNmrFq1Ck9PT86dO0eHDh3kjvZVsrKyCvxmbqVKlWL27NlERUVRtmxZWrduzeTJk0lJSZE72n82fvx4KleuzIYNG3BxcWHFihXo6uoCULt2bTIzM0lPT5c5Zf6pXr06p06donz58hgYGHD+/HmWLVtGmzZtaNGiBaGhoUyaNIknT54QGBiIvb096urqcscWiqCWLVtSp06dAj9b+NSpU3Tr1g1bW1t27txZYFejFDZJSUm0aNGCbt26kZ2dnXN7hQoVmDJlClu2bJExnSAI30v0FBaEIsDPz4+lS5dSunRp9PT0KF26NDo6Oujp6dGoUSMkSRI9n76BJEksWrQINzc3Nm3axJAhQ+SO9EXDhw8nMTGx2MygEvLGw4cP2blzJ97e3kRFRdG+fXscHBzo168fVatWzTkuKyuLIUOGcOjQIQ4fPlxsN1Tr1q0bGhoa6OnpceHCBa5cucLWrVsxMjL65DiFQlGsZuzdunULV1dXTpw4gaurK7NmzcrV/tX5QVVVleDgYExMTOSO8lU+FuFdXV2RJIlly5ZhZ2dXqF73MzMzKVGiBBEREYSGhlK9enVKlizJhQsXsLS05Pz586xYsYKIiIg83TCzoHn37h1+fn5MmTKFhIQEqlWrxpAhQ3B0dCyU+x8IhZerqyuRkZGcOHFC7iifFRQUhJ2dHU5OTixbtqxYve7mlaioKFauXMnt27fp2LEjEyZMoGrVqsTGxlK5cmXKly9PRkaGKL4LQgEmNpoThGIkOzubjIwMYmNjqVWrFuXLl5c7UqHn6enJmDFjWLp0KePGjZM7zmdJkoS2tjazZ8/G2dlZ7jhCESBJEpGRkfj4+LBjxw7i4+MxMzPDwcGBHj16MHz4cMLCwjh69CitW7eWO65sHjx4QEBAAHv37qVp06a4urrStGlT0tLSCAkJ4dGjR5iamhablRpv3rxh7ty5rFq1iq5du7JixYpC2ctUkiSUlZU5ceIEXbp0kTvON0lNTWX+/PksXbqUbt26sWrVqkLz+zdz5kz09PSwsrL65HZ3d3f8/PzQ0dGhT58+9O/fX6aE+UehUHDq1Cm8vb3ZvXs3kiTRq1cvHj58yNWrVwkKCkJfX1/umEIxc/z4cbp3786LFy/Q1NSUO84n/P39GTBgANOmTWPevHmF6oJYQWZmZkbbtm0xMDDAx8eHJ0+e0KtXr5yVUqNHj6ZNmzYypxQE4Uu+piiMJEn/+u/DYYIgFFTv3r2TnJ2d/3a7QqGQIU3Rsn37dklVVVWaNWtWgfx+Xrt2TQKk+/fvyx1FKIKysrKk0NBQaciQIVLZsmUlFRUVqXTp0tLGjRulrKwsueMVWPfv35eGDBkitWrVSoqKipI7Tp7Kzs6WNm/eLFWtWlXS0dGR9u/fXyCfK79WZmamBEhhYWFyR/nPoqKipC5dukglS5aUZs+eLb17907uSP+qbt260o0bNyRJkqSbN29KM2fOlAYOHCgdPXpUUigUUmpqqswJ897NmzelX3/9Vapdu7akrKwsmZmZST4+PlJKSookSR+ej52cnCR1dXUpODhY5rRCcZORkSFVqFBB8vLykjvKJ7Zs2SIpKytLHh4eckcpcpo3by69f/8+5/8dOnSQPD09pWPHjkkDBw6UbG1tpcTERBkTCoLwb/6nlvvFeq9YVyEIRUDp0qXR19cnISHhk9vFlfLvN2DAAAICAliyZAljx45FoVDIHekThw8fpmnTptSpU0fuKEIRpKKiQrdu3Vi5ciVt2rShYsWK6Onp4eLiQq1atZg4cSJXrlz5pg3qipI1a9YQFBTEs2fPPrm9du3abN26laZNm7Js2TKZ0uW98+fP06FDB8aMGYOrqytRUVH07NmzUL/2fOyXWJA3mvs3zZo14/jx42zdupX169ejq6tLUFCQ3LH+UUREBCoqKjRv3pzU1FTs7OxITk5GS0uL4cOHY2ZmVuBee3PLixcvWLlyJe3ataNZs2YEBQUxduxYHj9+THBwMAMHDqRMmTLAh+fjdevW4erqirW1Nbt27ZI5vVCclChRAisrqwLVV3jlypX8/PPPrFmzhilTpsgdp0h5//491tbWjBw5koMHD7J3714yMzNxcXGha9euTJs2jfPnzxfqPvaCIHwgisKCUEQMGDCAypUryx2jSLK0tOTIkSN4eXkxaNAgMjMz5Y6UIzg4GHNzc7ljCEXY69ev6d69O0+fPuXSpUscP36cp0+fMmPGDM6dO0ebNm3Q1dVl8eLFPHjwQO64+ermzZtYWVnxyy+/MGTIEC5fvszTp09ziqLly5cvdP10v8azZ88YMmQI+vr6NGzYkOjoaKZPn06pUqXkjvbdsrKygMJdFIYPF4UHDBhAdHQ0lpaWWFtb07t3bx4+fCh3tL85e/YsGRkZPH78mE2bNlGzZk2WL1/O77//zvnz51FWVmbPnj1yx8w1aWlp7Nq1C2tra2rUqIG7uzvGxsZERkZy9epVJk6cSPXq1T97XyUlJRYtWsTChQvp378/GzZsyOf0QnFmY2NDcHAwaWlpsuaQJImFCxcyYcIEtm3bxsiRI2XNUxSVLl0aNzc32rVrx6FDh/Dw8KBq1aqcPHmSp0+fcu3aNZSVlaldu7bcUQVB+E6ip7AgFDHSP2wq9+zZMzQ0NMTO1N8hIiICc3NzOnTowK5duyhdurSsed6+fUulSpUICgrC1NRU1ixC0fTy5Uu6d+9ORkZGzuZP/9fdu3fZsWMH3t7e3L17l86dO+Pg4ECfPn2oWLGiDKnzT1BQEDNnzsTf359169Zx+PBhKlasSLly5YiJieHt27cEBwcXmZ57GRkZrFixgvnz56Ojo8OqVaswNDSUO1auevPmDRUqVODy5ctF5ucGcOXKFVxcXLh+/TqzZ89m/PjxBWZzoJcvX7Jnzx5iY2MJCQmhd+/ezJo1K2cDo8mTJ5OWlsaqVavkjvqfKRQKTp8+jbe3N/7+/mRmZmJnZ4ejoyNdu3ZFRUXlm8+5adMmnJ2dWbRoEVOnTs2D1ILwqeTkZCpXrszevXuxtLSUJYMkSUybNo3ly5fj5+eHra2tLDmKuszMzJzN+j4+P/n7+xMaGkpCQgIxMTEMGTKEiRMnyhlTEIR/ITaaEwQB+LAb/KZNm0hPT8fOzg5jY2O5IxVa0dHRmJqaoqOjw4EDB2Td0C8gIICBAweSmJhYJGboCQXL06dPMTU1RU1NjaNHj/7rSgRJkrh48SI+Pj74+vry9u1brKyscHBwwMLCAjU1tXxKnr+0tbWJjIxEU1OTlJQU7ty5w8mTJ2natCmGhoY5S78Lu8OHDzNu3DgSEhJYuHAhI0aM+E+FrIIuKSmJSpUqce3aNVq2bCl3nFylUCjYvHkz06ZNo1q1anh6ehaYzfQyMjK4fv06/v7+3L9/n40bN5KSkkJ8fDwODg64ubnRt29fuWN+s5iYGLy9vfHx8eHBgwd069aNQYMG0atXL8qWLfvd59+zZw8DBgzA1dUVDw+PQt26RSgcLCws0NbWlmWWukKhYPTo0Xh5eREQECAmROSRFy9esG3bNubOnUvHjh2ZOXMmnTt3RqFQ4OfnR1paGj/++CONGjUSnz8EoYD7mqKwaB8hCEVMSkoKa9as4erVqzm3NW3alPnz59O6dWvc3d1JSkqSMWHh1rhxY86cOcPz588xNjbm5cuXsmU5fPgwxsbG4g2ZkOsePHhA586d0dDQ4Pjx41/VmkZJSYkOHTqwatUqnj59yt69e1FTU2PgwIFoaWnh7OxMeHh4kesN2rJlS/bt2wdA2bJl+eGHHxg/fjzm5uaUKVOGjIwMmRN+n7t372JtbY2VlRWmpqbcuXOHkSNHFsmCMBSd9hGfo6yszIgRI4iOjkZfX5+uXbvi4ODA8+fPZcuUmZnJ06dPuXLlCi9evMDV1RVfX1/KlSvH1atXsbOzY9SoUYWqIJyQkMCaNWvQ09OjcePGBAQEMGrUKB49ekRISAiOjo65UhAGsLOz49ChQ3h6ejJixIicntiCkFdsbGw4ePBgvr+WZ2VlMXjwYHbs2MGRI0dEQTgPrVu3jri4OM6fP4+pqSmrV69m2bJltG/fnocPH2JtbU3Lli3F5w9BKCJEUVgQipiIiAgCAgJylnnv27ePu3fvoq6uzk8//UTJkiW5fv26zCkLt1q1ahEeHg6AoaEhjx49yvcMkiSJfsJCnrhz5w6GhobUrl2bo0ePUqFChW8+R4kSJbC0tGTHjh3Ex8ezYsUK4uLi6NKlCzo6OsyYMYObN2/mQfr8t2bNGgwMDHj16hU9evTAw8ODbdu2kZCQgEKhKDBL9L9VSkoKv/76K82bNyc5OZkrV66wevVqNDU15Y6Wpz4WhYtq0RugcuXKbNq0idOnT3Pjxg0aN27MqlWrch57fvrtt99wdnZm9+7dXLx4kVGjRmFlZcXs2bNp2rQp9+7dY9SoUfme61ulp6ezZ88ebG1tqV69OvPnz6dTp05cuXKFyMhIJk+eTM2aNfNkbFNTU0JDQ9m7dy/29vakp6fnyTiCAGBtbc3z58+5ePFivo2Znp5O3759CQ4O5sSJExgYGOTb2MVV27Zt0dXVZcqUKZw9e5ZHjx4xZcoUwsPDGTVqVIHaX0UQhO8j2kcIQhEzcOBAWrRowbRp0wDo168f4eHhzJ07l/v37/Pw4UM2b95MiRIlxDLD7/T27Vusra2Ji4sjJCSExo0b59vYN2/epHnz5ty9e5f69evn27hC0RYVFYWJiQlt2rRh9+7dud43++nTp/j6+uLj48OVK1do06YNDg4O2Nvb/+PGSoVJvXr1sLOz49WrVzx58gRJkujTpw916tTB0NBQ9j7kX0OSJHbs2MGUKVNQVlZm6dKl9O3bt9i8Xjx69IjatWsXm+fWrKws1qxZw6xZs2jQoAGenp7o6enly9iZmZloamoSHx+fM8M1KyuLmzdvEhISwsuXL5k/f36BvRAhSRJnz57F29sbPz8/0tPT6dWrF46OjpiYmOT7bPMbN27QvXt3dHV12bt3b67NRhaE/6tDhw507dqVxYsX5/lYqamp9OrVi6ioKEJCQmjWrFmej1ncnTlzhvHjx7Nz507q16/PkiVLcHFxyXlO0dPTY9OmTejq6sqcVBCEfyPaRwhCMdShQweOHz9OcnIy8fHxqKioMG/ePG7evMmNGzcYPHgwJUuWRFzo+X7ly5cnODiYH374AUNDQyIiIvJt7ODgYBo0aFAsihZC/oiIiMDIyIhOnTqxb9++PClg1qhRgwkTJhAREcGNGzcwMzNj+fLlaGtrY2ZmhpeXF8nJybk+bl77OMPS1NQUdXV1Nm/ezOrVq/nll19YsmQJAwYMYNy4cTKn/HcREREYGhoyfPhwhg8fzu3bt/npp5+KTUEYinb7iM9RVVXF1dWV6OhomjRpgr6+Pk5OTiQmJub52ImJiXTq1IlTp05Rrlw5ypUrR8WKFdHX16d///7cvHmTLVu2FLj3K3fv3mXOnDk0aNAAQ0ND7ty5w7Jly3j+/Dnbt2/H3Nxclt8fXV1dzpw5w7179zAxMRGtwoQ8Y2try/79+/N8nNevX2NmZsbdu3cJDw8XBeF80qlTJ9zd3XNeByZPnkzZsmVJTEzk+PHjPHz4UBSEBaEIETOFBaGISUlJYfbs2Vy9epWLFy9iZmaGv78/QM4usmvXrkVdXZ3BgwfLGbXIyMzMZOjQoRw8eJCDBw/SuXPnPB/T1NSUpk2bsnLlyjwfSyj6zp49S48ePejZsydbt27N14KGQqHg9OnT+Pj4sGvXLjIyMrC1tcXBwYHu3bsXiuKcQqFAWVmZkydP4uLiwvLlywkKCuL27dvUrFkTc3Nz2rRpQ4MGDeSO+lkJCQnMmDGDjRs3Ymtry9KlS6lXr57csWRx584dGjVqxKNHj9DW1pY7Tr47fvw4o0aNIiEhAQ8PD4YOHZrz3iEvrFmzhl9//RU7Ozvs7e3R19enXLlyAPz++++Eh4fnS/Hp3yQlJbFr1y68vLw4d+4cTZs2ZdCgQQwcOJBatWrJHe8Tz549w8zMDEmSOHLkCDVq1JA7klDEfFytFh0dTaNGjfJkjJcvX2JmZkZaWhohISF51n5F+LuoqCiaNGnySRul+Ph41q9fz6lTp+jZsyeurq4yJhQE4Wt9zUxhURQWhCLq0qVL1KtXj0qVKuXclpGRQcmSJTl06BBz5szh0qVLMiYsWhQKBWPHjmXz5s3s3r0bS0vLPBsrNTUVTU1N9u3bh4WFRZ6NIxQPx48fp2fPnjg4OODp6ZmnBaB/k5aWRlBQED4+Phw6dIgKFSpgb2+Pg4MD7dq1K9AzVv38/Dh16hTbtm3DyMiIPn36YG5uXqALMllZWaxbt45Zs2ZRvXp1VqxYUew377l16xbNmjXj2bNnaGlpyR1HFhkZGSxdupT58+fTqlUr1q5dyw8//JBn4127do39+/cTExNDcnIyqamplClThhIlSuDg4ICtrW2ejf0lGRkZBAUF4eXlRWBgIBoaGgwYMABHR0fatGlToJ+PXr16haWlJc+fPyckJESsKhJylSRJNGrUCCcnJyZPnpzr53/y5AmmpqaUKlWKI0eOUKVKlVwfQ/i7hIQEFi9ezIULF0hISMDV1RUXFxdSU1NRU1Pj9u3bVKtWTfw8BKEQEUVhQRD+Ue3atTl//nyBLlgUNpIk4ebmxuLFi/Hy8qJ///55Ms6hQ4fo06cPSUlJqKur58kYQvEQGBiInZ0do0aNYunSpQWqyJGUlMTu3bvx8fEhPDychg0b4uDgwMCB8JWkrwAAIABJREFUAwtkgcPd3R0dHR38/f2xtbVl4MCBxMbGsmLFClJTU3n9+jVjxozByMhI7qgAnDhxgrFjx/Lw4UPmzp3L6NGjKVGihNyxZHfjxg1atGjBy5cvqVy5stxxZHX//n3GjRvHwYMHGTNmDPPmzaN8+fK5cu6srCwuX76cs/Ftq1atqFKlCk+ePOHp06e8fPkSe3t7KlasmCvjfS1Jkrhw4QJeXl74+fmRmpqKra0tjo6OdO/evVD9jaSmpmJnZ8e1a9c4cuQILVu2lDuSUIRMmjSJ8+fPc/r06Vw9b2xsLCYmJtSoUSPn4rCQPxYtWsSZM2fYsGEDUVFRrFixgh49erBw4UK0tbWZOHEiffv2LdIbsQpCUSOKwoJQjEVERFCpUiVUVVWJjIykfv36VKpUiWfPnqGrq0v//v1p1aoVU6dOlTtqkbNs2TImTZrE6tWrcXFxyfXz//LLL9y5c4cjR47k+rmF4mPPnj3079+fadOmMXfu3AJVEP6/7t+/z44dO/D29ub27dt07NgRBwcH+vXr98lqiIIgKysLVVVV4uLi6NGjB127duXHH38kPT2d1atXc+zYMVlnoD548IDJkyeze/duhg0bxqJFi6hatapseQqaq1ev0rp1a5KSkvK9IFlQHTp0iDFjxpCens4ff/xBv379vvv5YtWqVQQGBpKWlkaVKlVyLqq2aNEil1J/m9jYWHx8fPDx8eHOnTsYGRnh6OhInz590NDQkCVTbsjIyMDR0ZGjR48SGBiIvr6+3JGEIiI8PBwjIyOeP3+ea68ht27dwsTEhObNm7Nv3z7KlCmTK+cVvo6JiQnDhg1jwIABAPTt25fmzZszZ84cfv/9d3x9ffH19S2wrbAEQfi7rykKI0nSv/77cJggCIXJkiVLJENDQ6lWrVpS3759pcGDB0tNmjSR+vTpIw0ZMkSqUqWKZGVlJXfMImvLli2SioqKtGDBAkmhUOTaeRUKhaSjoyMtW7Ys184pFD9eXl6SioqK5O7uLneUb6JQKKTLly9L48ePl6pVqyapqqpK1tbWkp+fn/Tu3Tu5433i8uXLUrdu3aQnT57k3NanTx9p/fr1suR59+6dNHfuXKl06dKSnp6edPHiRVlyFHR//fWXBEhv376VO0qBkpqaKs2cOVMqWbKk1LVrV+nWrVvfdT4dHR3p5s2bkiRJ0osXL6S2bdtKDRs2lK5du5Ybcb/Kq1evpA0bNkgGBgYSIDVu3FhasGCBFBcXl28Z8kNWVpbk5OQkqaurS8HBwXLHEYqIrKwsqXLlytLmzZtz5XyXL1+WKleuLNna2kppaWm5ck7h2wQFBUmTJk2SoqOjpcDAQKl27dpSbGysJEmSlJKSItWtW1fau3evzCkFQfgW/1PL/WK9V77GgYIg5KmffvqJFStW8PDhQ3bt2sWsWbOIjIzEy8uLCRMmEBwczLBhw1AoFHJHLZKGDh2Kv78/8+bNY/Lkybm2e/rdu3eJjY3F3Nw8V84nFD/r169nyJAhLFu2rNCtFFBSUqJNmzb88ccfPH78mMDAQCpUqMCwYcOoVq0aw4YN4/jx42RnZ8uSb/fu3cyfP583b95w5MgRtLS0iI6O5t27d5w7d443b97key8+SZLYs2cPTZs2Ze3ataxbt44zZ87Qrl27fM1RWHz83SkMGxzmJ3V1debPn8/169dRVlamZcuWTJ8+nXfv3n3zud68eUOtWrV49eoVAFWqVKF///5kZGTQtWvXnM1x80JmZiYHDx6kb9++aGlpMX36dFq1asXFixe5desWM2bMoG7dunk2vhxUVFRYt24dY8eOxdraOk+/v0LxoaKigrW1da5sBHnmzBmMjY0xMzNj165dqKmp5UJC4Vt16dKFSpUq4eDggL+/P127duXp06cAREZGEh8fj4mJicwpBUHIbaJ9hCAUA5Ik5Sz1jI2N5dChQ2RmZtKlSxcaNWqUs9O3kPuOHTuGjY0N/fr1Y/369d9daFi5ciXLli0jNja2QC/3Fwqmj61NNm7cyLBhw+SOk2tSU1PZv38/Pj4+HD16FC0tLQYMGICDg0O+9tEMCgpi//79XLx4kXfv3pGQkIBCoaBv3768efOGGjVqsGzZsnzLExUVxdixYwkPD2fcuHHMnDkz13rCFlVnzpzBwMCA9PR0SpYsKXecAkmSJPz9/Rk/fjwlSpRg5cqV9OzZ85vOsXHjRg4fPsyECROIjIwkODgYCwsLtLS08Pb2ZuLEibnW6kCSJC5duoSXlxe+vr68ffuWnj17MmjQIMzNzQtVn+DvtWTJEqZNm8a6desYMWKE3HGEQm7//v3079+fhISE/7zHRUhICLa2tgwaNIg1a9bIutltcXfjxg3KlStHnTp1ePfuHRcuXMi5YFe3bl2MjIzy9T2MIAjfT/QUFoRi7n8XgwFevHjBpEmTUFVVpUqVKly+fJmaNWuybds2GVMWfRcvXqRHjx506dKFHTt2fNcMCAsLC+rUqcPatWtzMaFQ1EmSxIIFC5g3bx7e3t7Y29vLHSnPxMfH4+fnh4+PD3/99RctWrTAwcGBAQMGoK2tnS8Z3r9/z6tXr7h06RJpaWk8efKEGjVqYGlpSdmyZf/23JzbXr16xZw5c1izZg2mpqYsX76cxo0b59l4RcmpU6fo0qUL2dnZojjxL5KTk5kzZ07OZkQrV66kXr16X33/efPmERQURIUKFWjfvj0TJ05EQ0ODadOm8ebNm+9+nXvw4AE+Pj54e3sTHR2NgYEBjo6O9O3bt1j3i960aRPOzs4sWrSo0K0WEQqWd+/eUblyZXbs2IGtre033z8gIIB+/foxbtw43N3dxWQHmaSnp2Nvb8+LFy/Q0NCgU6dOzJgxA/iwT8LZs2cpW7YsP/zwg9hkThAKGdFTWBCET+zatUvS1taWoqOjpdTUVEmSJElLS0u6f/++zMmKvhs3bkg1atSQunXrJiUnJ/+nc7x7904qVaqUtH///lxOJxRlCoVCmjp1qlSyZEkpICBA7jj56vbt29KsWbOkevXqSUpKSpKxsbG0efNm6fXr13k25ud6iCcnJ0tnz56Vtm7dKv3555+Sj49PnoydlZUlbdiwQapcubJUv3596eDBg7na07w4OHbsmPQ/kyGErxQZGSl16tRJKlWqlLRgwYIv9gNVKBRSSEiI9Ouvv0qhoaFSWlraZ18TP75H+VavX7+WNm3aJBkZGUmA1KBBA2nu3LnSvXv3/tP5iip/f3+pRIkS0pQpU8RzhPBdevbsKQ0ZMuSb7+ft7Z0ne28I3279+vVSkyZNpIiICOngwYNSx44dJQcHh0+eh4ODg8XPSRAKIURPYUEQXr58SVJSEvBhifXAgQNp1KhRzjKvtm3b8vjxYzkjFgvNmzfn9OnT3L9/HxMTk5yfybcICwsjOzsbY2PjPEgoFEUKhYKxY8eycuVKDh48iI2NjdyR8lXjxo2ZN28e9+7d4/Tp0zRp0oTJkydTrVo1fvrpJw4cOEBGRkaujvlxptONGze4f/8+iYmJ2Nvb07t3bxITE7lz5w6Ojo68ffs2V8c9e/Ys7du3Z/z48UycOJGoqCisrKzEzKtvlJWVJfoJf6MWLVoQFhbG2rVrWb58OS1btiQkJOSzx/r5+TFhwgQuXLiAk5MTRkZGvHz5MufrkiSRkpLyTUvRs7KyCAoKwt7eHi0tLaZMmUKzZs04d+4cMTExzJ49Gx0dne9+nEVJnz59CAwMZM2aNTg5OcnWh10o/GxsbDh06NA3/Q6tW7eOwYMH88cffzBjxgzxOiWzGzduYGlpSevWrbGyssLX15f4+Hh8fX0BOHDgAG5ubuLnJAhFlCgKC0IRN2PGDPT19Rk/fjxz587F39+fQYMG4eHhgZWVFSVKlKB169ZyxywW6tWrR3h4OO/evaNz5845mzd8rcOHD2NoaCh6QAtfJTs7mxEjRrBt2zaOHDlC9+7d5Y4kGyUlJfT19fH09OTZs2fs2rUL+LAhZ/Xq1Rk1ahRnz57NtQ0hw8LCMDMz4+HDh1SqVAlvb2+0tbVp3rw5CxYswMLCIifD93r69CmOjo4YGBjQtGlToqOjmTZtmtio5z/KysoSy2P/A2VlZYYMGUJ0dDQjR47k0aNHpKen/+24rVu30q9fP44dO8a9e/do374906dPJyEhAfjQ09nT0/OrxlQoFPj6+lKzZk1sbW3JyMhg586dPHv2DE9PT/T09EQR4wtMTU0JDQ1lz5492Nvbf/bnJQj/xsrKisTERM6ePftVxy9ZsoTRo0ezadMmxo4dm8fphK8xYsQIDh48yIULFwCoXbs2Tk5OrFmzhoyMDEJCQjAzM5M5pSAIeUUUhQWhiFu0aBHHjx+nYcOGuLi40LFjR+Li4rh58yZ6enp4enr+580hhG9XvXp1Tp06Rfny5TEwMODevXtffd/g4GDMzc3zMJ1QVGRmZuLg4MDevXsJDQ3F0NBQ7kgFRsmSJenZsye7du0iPj6eJUuWcPv2bQwMDGjQoAGzZ88mOjr6u8bYunUrv/76K507d+b9+/dUrFgRXV1dgoODAejVqxcBAQHfNUZ6ejoeHh40atSIqKgowsPD8fHxoWbNmt913uIuOztbzBT+DpqamowfP54hQ4Z89sJErVq1SEhIID09HUmSmDlzJmlpaTn9g1esWMHr16+/erzmzZszd+5cnj9/zt69e7G1tRUbBH4DPT09wsLCOHPmDNbW1qSkpMgdSShkqlatir6+Pvv37//icZIkMWvWLKZPn46vry9Dhw7Np4TCv2nRogXLly//5PNgnz590NPTo0+fPuzevZvBgwfLmFAQhLwkNpoThGImOzsbFRUV3rx5g4aGBgqFAmVl5Tzf+Ej4VGpqKr179yYyMpKjR4/SokWLLx4fFxeHjo4O169fR1dXN59SCoVRWloa/fr149y5c4SEhPDDDz/IHalQePToETt37sTb25sbN27Qrl07HBwcsLe3p2rVqt90Ljc3N65cuYKPjw/ly5cnJSWFIUOGYGFhwbBhw8jKymLnzp04Ojr+p6yBgYGMGzeOV69esWjRIoYPHy5mt+aSffv2MWzYMF69eiV3lCIpJiYGd3d3FixYQI0aNQD466+/6NWrFwcOHMDBwYGAgAAaNWr0t/t+fJ8i3q/kvtjYWExNTalatSqBgYFoamrKHUkoRH7//XfWr19PTEzMZ/82FQoF48ePZ8OGDezZswcLCwsZUgqfc+/ePerXr//Jbenp6aipqZGVlUXr1q2Jj4/nxYsXMiUUBOF7fM1Gc2KmsCAUIwqFAhUVFbKystDQ0CAiIgJ3d3eCg4PFB6x8VqZMGQ4cOIChoSFGRkacP3/+i8cHBwdTs2ZNmjdvnk8JhcLo3bt39OzZk0uXLhEWFiYKwt+gVq1aTJkyhevXr3Pt2jW6dOmCh4cHNWrUwMLCgh07dpCamvpV5/rll1+QJIlevXrlFFqUlJQYNmwY8KG49V8KwjExMVhaWmJjY0OPHj24c+cOTk5OoiCci0T7iLyTkpJCo0aNGDt2LJmZmcCHVQ3t2rVj6dKl/Pjjj6SlpX22IJyZmcn9+/cBxPuVPKCjo8Pp06dJTU3FyMiIZ8+eyR1JKERsbGy4e/cut27d+tvXsrOz+fnnn9myZQvBwcGiIFzATJs2DTMzMxYtWkRoaChpaWmoqakRFhaGqqoqXl5ebNu2Te6YgiDkIVEUFoRi4uPukv97aWybNm1o3rw5FhYW6OjofHOPW+H7qKmpsXPnTuzs7OjWrds/bswDH/oJ9+jRQ3wYFv7R27dvMTc3586dO4SHh9OkSRO5IxVaLVu25LfffuPhw4ccPXoULS0tRo4cSbVq1Rg0aBBHjx794qY6VapUwdfXl/79+2NsbMzVq1fZvn17zpLZiRMn8scff3Djxo2vypOcnMzUqVPR1dXl/fv3XLlyhZUrV1KxYsXcesjC/xDtI3JHUlISCoXik9sGDhzIkydPaNWqFXXq1AGgRIkSAPTr14/hw4fz888/f/Z8KioqxMXF5W3oYu5je6ty5crRqVOnb2pvJRRvDRs2pGnTpn9ri5SRkUH//v0JCAjg2LFjGBkZyZRQ+CfDhg3jwoULXLx4kR07dmBhYcHo0aMxMzMjNjaW1q1b06NHD7ljCoKQh0RRWBCKCSUlJVRUVFBRUSExMRF3d3caN25Mnz59sLOzY8mSJdSoUSPXNloSvo6KigobNmzgl19+wdLSkj179vztmPT0dI4fPy76CQv/KCkpCRMTE+Lj4wkPD0dHR0fuSEWCiooKXbt2ZcuWLcTHx7NlyxZev36NpaUlHTp0+OJ9y5Qpw88//8z06dMJDAyka9eu+Pv706xZM2rUqEFYWBiOjo5fLC5LkoRCoeDKlSukp6eza9cujh079q/tZoT/LisrSxSF/6OkpCSWLl1K27ZtOXXq1Cdfi46OJjIykpo1a5KZmcnJkyf56aefsLKyYvv27SgUChYvXoyzs/Nnz52RkcFff/0FIN6n5KGKFSsSEhJCw4YNMTAw4Pr163JHEgoJGxubT/oKv3//HltbW8LCwjh16hTt27eXMZ3wT3r06MG6devQ0NBgxowZeHt7o6SkRPny5Rk6dCiurq5yRxQEIY+JnsKCUAxIksS7d+9YunQp/v7+OZvMjRw58j/3tBRyn4eHBzNmzGDDhg05y8wBjh07hpmZGYmJiWhoaMiYUCiIXrx4gampKZIkERISQrVq1eSOVOQlJCSwa9cuTE1Nadiw4T8el5mZya+//opCoaBfv360aNECVVXVnAt07dq1Y+7cuf+6nFahUKCkpCRWCuSDP//8Ezc3Nx48eCB3lELFy8uLefPm0bRpU+bOnUvLli0/Ka67ublx584dduzYwd69e3Fzc2PQoEG8fv2a7du3Y2Fhgaen52fPrVAoCAsLQ1NTk5YtW+bXQyrWMjIycHBwICQkhKCgIDp27Ch3JKGAO3/+PB07duTJkyeUK1cOa2tr4uLiCA0N/eLrpCAvhULB+/fvmT17Nqqqqnh4eODk5ET16tWZPHkyUVFR/3oRXBCEgutregqLqRCCUAwoKSkRHByMj48PM2fOxMHBAWXl/79QICsrK2cmsSCfqVOnUrFiRUaMGEFSUhKTJk0CPvQT1tfXFwVh4W+ePHlCt27dKFeuHEeOHBGbA+WTypUrM2rUKICczTo/59q1a1y4cIHFixf/7UPV6dOn6dy5899u/9z5/un8Qu4T7SO+7OMmb5cvX8bf3x9dXV2sra3p168f9vb2lCxZMue4/y06OpqUlBRevXrF1q1bGT58OOPGjQPA0tKS6dOnExYWRufOnf82pkKhoEKFCqIgnI9KlizJzp07cXFxwcTEhH379tG9e3e5YwkFWPv27dHS0sLX1xc/Pz9evXpFeHg4tWvXljua8AXKysqUKVMGNzc3LC0tWb58OYcPH+bkyZOULVtWFIQFoRgQnzIEoZiws7MjJiaGQYMG/a3AID4AFxxOTk7s2LGD6dOnM336dCRJyuknLAj/W1xcHIaGhlSpUoXQ0FBREJbJlwq2Fy9epEKFCjRt2hSA27dvs2HDBuzt7ZkxYwYtW7akUqVKOcdLksT169f58ccfWblyJZmZmX/ryyrkLdE+4suUlJR48eIFv//+O+XLlyckJARra2vU1NQoWbJkzgZyWVlZn9zPw8ODbt26MWvWLCIjI9HV1c35mr6+Pmlpabx+/fqzY6qqqtKqVau8e1DCZ6moqLB+/XrGjh2LlZUV/v7+ckcSCjBlZWVMTExwc3Pj/fv3oiBciGRlZVG+fHn2799PUFAQGhoa1K9fX7TqEYRiQrzrFYRi5OMMtCtXrnD69Gk0NDQwNzdHQ0MDNTU1ueMJ/6Nfv35oaGjQu3dvHj16RFRUlOgnLHwiOjqabt265WzsUqZMGbkjCZ9hY2NDREQElpaWJCcn07JlSxo2bEj//v2xsbH52/FKSko0btyYhQsXsnr1aiwtLalfvz4XL16kcePGYrVAPsjKyhKrZv6X6OhovL29KVeuHM7OzlSoUIG9e/dSoUIFpk6dyps3b7C1teX8+fPo6enltDj5uIHcR3Xq1MHZ2Znw8HAyMzMJCwvDxMSEyMhIYmNjuXPnDt26dZPjIQpfoKSkxOLFi6lYsSL29va8fv2aESNGyB1LKIAePHjA8ePHSU1N5eDBg6KVVQF3+vRpGjVqRNWqVXMuhGpqavL777+TlJQEiJUzglBciL9yQShGPs5oK1GiBK6urowdO5Zt27ahrq5O165dadq0KXp6eqirq+csARXkYW5uTkhICKamppQqVYpmzZrJHUkoICIjIzE1NaVDhw7s2rWLUqVKyR1J+Ac1a9Zk06ZNHDp0CG1tbUqVKoWKispn+yt+XJavpqaGmZkZ0dHRnDhxgvr16xMYGIitrS1NmjRh8ODBDBw4UHxQyyPiQ/CH74GKigpRUVE4ODjQvn177t27h6urKx4eHlSrVo3BgwejoqLC/fv3qVSpUs7v9Je+d+rq6piZmWFmZgZ86Ft76NAhgoKC8PX1FRe3CrApU6agqamJs7MzSUlJTJ06Ve5IQgESExODiYkJDRo04PXr11y8eJE6derIHUv4B8nJyaxevZqSJUuiq6tLs2bN0NXVpW7dup+06Snur4WCUFyIjeYEoZj5OFu4fv36XL9+HXV1dS5dusTIkSO5c+cO2dnZeHt706tXL7mjCoCJiQlnzpzB2NiY3bt3o66uLnckQUZ//fUXZmZmdO/eHW9v77/NxhMKridPnrBhwwbu3bvHrVu3aNWqFfr6+jRu3BgDA4O/He/m5oaamhrTp08HID09nQMHDrBkyRIePHjA2rVr6d27d34/jCLvjz/+wMfHh4iICLmj5KuMjAy8vb1Zu3YtI0eO5Oeff2bmzJloamoyYcIEbt26xYoVKzA3N8fW1haA169fY2BgQFpaGh4eHtja2v7rLOvs7GyUlJRQVlb+Yj9uoWDavXs3AwYMYPz48bi7u4vNL4WcC9Xt2rXD398fR0dHSpUqhY+Pj9zRhM/4eAF69uzZ+Pr6MnToUOLi4ihVqhRaWlo0bNiQli1b0qBBA7FqRhCKgK/ZaE68ExOEYubjB7Bu3boxbNgwtmzZwpo1azA2NmbmzJn4+vrSrl07mVMKAJmZmfz1118sXryYmzdvYmZm9o89F4Wi7/Tp03Tr1g0bGxu2b98uCsKFSExMDFZWViQkJFC7dm2qV6/OmTNnWLFiBatXr6ZWrVpcuHDhk/sEBQVhbW0NQFpaGmpqavTt25cBAwbQtWtX2rVrR3R0NKNGjaJPnz6cPXtWjodW5BTH9hGHDh2iXr167NmzBycnJ4YOHQpAixYtOHPmDPBh1ntEREROL2BJkqhQoQKbN29m06ZNzJ49m40bN/7rWCoqKjnvQ5SVlb+5Z6WYpCKvPn36EBgYyOrVq3F2diY7O1vuSIKMLly4gJGREV26dGHv3r2ULl0aGxsbAgMDc/qLCwXLxws5o0aNwsDAgLZt2+Li4oKuri7JycmcOnWKxYsXs2fPHpmTCoKQX8SaAEEoZhITE1m/fj0vXrzg8OHDdOrUCXt7exo3bkzdunXljif8L+fOnSMlJYVBgwbRp08funfvjrGxMcHBwaJXWzETEhKCjY0Nw4YNY+XKlWJ2XSFz4MABevXqxezZswF48eIFEydOZNiwYRgaGhIeHk716tVzjpckibZt29KiRQuAnBYhY8eOJTU1ld9++43s7Gzc3Nzo0qULycnJTJkyhfXr19O8efP8f4BFSHFoHxEWFkZ8fDxGRkZUrVoVSZKoWbMmQUFBnxzXq1cvpk2bxrRp03j9+jXv37/n5s2blCpVCm1tbd69e5ezM/3QoUMJCwtj5MiRXxz74yy1j75mpunHQvCLFy/Ys2cPLVq0wNDQ8FsftpBLTE1NCQ0NxdLSktevX+Pt7S32pSiGTp48ibW1NT/99BMbNmzIuZhmaWnJ0KFDCQ8Pp2vXrjKnFP6JlpYWS5cupWLFigC0bt2alJQUYmJiuHDhQs77D0EQij7xqVIQihlNTU3U1NQYN24cBgYG2NvbY2ZmRp06dTh06BBLly7l+PHjxMXFyR212Dt8+DAdOnRAU1MTbW1twsLCUFVVxdDQkAcPHsgdT8gnBw8exMrKil9++YVVq1aJgnAhFBsby/v373P+//DhQ2JjY8nIyEBVVRVjY+NPdmnPzs7Oma0JH1pH/Pnnn1y5coXly5dTq1YtLl++jLa2NiNHjmTy5MlIksS1a9cAuH//Pu7u7kyaNInnz5/n3wMtArKysopsUTgxMREzMzOcnJzYv39/TsHGxMQEhUJBaGgozs7OTJgwgatXr1KyZEn+/PNPrl27Rvv27dm6dSsXLlzI6Se7evVq7t+/D8Dz589xcXH51wwfi8AfC71fM9NUSUkpp9/27du36dKlC3Z2djx69Oi/fBuEXNCxY0dOnTpFeHg4PXv2JDU1Ve5IQj4KDAykR48eDB8+nI0bN36yukJTUxNDQ0P2798vY0Lhn7x8+ZL4+HgyMjJyCsIflS1bljZt2uDi4kLTpk1lSigIQn4TnywFoZhRUlLCycmJLl26MGrUKDw8PDhx4gRhYWHs2bMHDQ0Ndu/ezbBhw+SOWuwFBwdjbm6e8//KlStz/PhxtLW1MTAw4Pbt2zKmE/KDn58fvXv3ZsaMGXh4eIj+jYXU8OHD8fX1Zd68eezdu5eFCxdSpUoVunXr9tnjVVVV+fHHH3P+v2bNGi5evMiMGTMoV64cAG/evOHGjRsAXL16lSpVqlCxYkWeP3+OsbExsbGxJCYmMn/+fLGM9xsUpfYRb9++zdlFHuDZs2fUrl2b27dv4+PjgyRJ+Pj4ULJkSapUqcKgQYPQ0NAgPT2d0aNHc/nyZZSUlMjMzGTYsGH8+OOP9OzZk6tXryJJEjGtoAUiAAAgAElEQVQxMYwcOZK6dety7tw52rZt+7cMDx8+5Pz585w6dYoTJ05w9+5dFApFznPZ9u3befv27Vc9ngoVKrBy5Ur++usvHj9+TJMmTfjtt9/IyMjInW+Y8E0+the5e/cupqamn/yuCUXXrl27sLW1ZcqUKSxbtuyzF6ptbW3Zv3+/aPdSAP32229YWFiwZcsWwsPDuXfvHgDnz58nMjJS5nSCIMhBFIUFoRgqV64cly9fZsGCBaSkpHDq1Cl69+6Nnp4e/fr1w9PTk7t37+YUHIT89+zZM65evUqPHj0+ub1cuXIEBQXRtm1bDA0NuXTpkkwJhby2detWBgwYgLu7O7NnzxYF4UKsbdu2rFu3jsTERFavXo22tja///47qqqqREVFcfr06ZxjFQoFQE5hMj4+nqVLl/Lzzz9jamoKQHR0NFFRUTx9+hQLCwvGjBmDjo4O7du3Z+XKlTRv3pwNGzawefNmYmNjSUlJAeDVq1csWLAAT09P0tLS8vm7UDgUpfYRvXv3Zt++fTmzcSMiItDR0eHp06fAh9med+/eRUVFBWNjY1atWsVvv/3GihUrMDQ0ZMeOHbx584bSpUvnbLzn5eVF27Ztyc7OZtOmTXh4eBAQEMCZM2f+thHq1q1bcXNzw83NjRMnTnDy5Em2bNnCsmXLci5qhoWFUb58+W96XG3atOHcuXMsX74cd3d3WrVqxcmTJ7/zuyX8Fzo6Opw+fZrk5GSMjIx49uyZ3JGEPLRlyxb69+/P4sWLmTt37j++L7GxseHBgweiyFgAjRkzBjU1NVauXMmff/7JkiVL2LZtG4MHD+b69esoFApRzBeEYkYUhQWhmPL09KR9+/b89ttvzJkzBxMTE6pUqUK5cuXIzs6mdevWYjmgjI4cOULlypU/O/OqVKlS7N69GwsLC4yNjcWH4SJozZo1/Pzzz6xZs4aJEyfKHUf4TpIkYWZmxpIlSzh+/DirVq2iQYMGADRo0ICYmBgsLS0JCQn5ZNZVUlISc+fOxdzcnDZt2qCiokJCQgIrVqxAQ0ODK1eu4OzsTIsWLViwYAEvX77k9u3bOb8z8fHxtGvXjqSkJMLCwujXrx9Xrlxh//79Of2NhU8V1vYR/v7+LF26lMTEROBDT/pz585x4cIFXr16BUDLli25f/8+Hh4e+Pv7c/bsWTp27EhaWhpjxozBzs6OzMxMVFVVKVOmDMnJyfzwww+ULVsWJycnmjRpgr+/P9bW1jnfox9++IFWrVp9NtPChQtxdHTkyJEjjB07lr59+9KpUyeSk5MZNWoU58+fZ9OmTf/p8SorKzNixAiio6Pp2LEjXbt2xcHBQbRLkUH16tU5deoUZcuWxcDAgNjYWLkjCXlg+fLljBgxAk9PTyZNmvTFY+vWrUvLli0JCAjIp3TC16pduzaLFy9GS0sLR0dHRo8eTVRUFI8fP+b06dP8/vvvYgNJQShmRFFYEIqpxMRE1NXVc2botG/fnmXLlmFoaIimpialS5emdevWMqcsvg4fPoyZmdk/9o9VVVVl69atDBs2DHNzcw4cOJDPCYW8smTJElxdXdm6deu/btokFAxpaWmsX78+Z0bu/6WkpIQkSZ/9ew4KCmLLli0cPnwYFxeXT2balS9fnmrVqjFmzJic2x48eMCjR49o27YtKioqdO/enffv3xMeHp6zCZixsTHwocAZHR1N1apVmTVrFu3bt2fPnj3s2rWL9PT0nBmjycnJnDx58pMZy8VVYWof8fbtW6ZOnUrlypVxd3enRo0aaGhoAB/6Sjs7OxMfH5/Te7dVq1bMnz+f0NBQdu/ezfz581m/fj2//fYbd+7c4erVqyQnJwOQkJBAcnIytWvXZu3atUyePJl58+YRHx9P3759/zVbdnY2PXr04OjRo7x//x5NTU10dXWxtLRk5syZdOjQgU2bNn33jPUqVaqwefNmTp8+zfXr12ncuDGrV68WRY18pqmpSWhoKA0aNMDAwIDr16/LHUnIJZIkMX/+fCZNmoS3tzfOzs5fdT8bGxvRV7iAMjIyYvDgwcyYMYMWLVrQpk0bjIyMGD16NNWqVSuUF0YFQfjvxF+8IBRTDg4OTJ06FS0tLZSUlDhw4ADr16/n0qVLtG3bFl1dXbkjFltZWVmEhISwcuXKLx6nrKzM8uXLqVSpEr1792br1q04OjrmU0oht0mSxJw5c1i0aBE7d+78qsKLIC9Jkjh06BDjx4/nzZs36Ovro6ur+9kltUpKSjkftHx9fdmwYQPh4eHUrFkTBwcH/Pz8qFmz5if3UVVVxc3N7ZPbdHV1ycrK4tKlS/To0QMvLy/S09NRV1fn6tWrVK9ePSfb9evXKVGiBO/fv+fKlSuEhoYiSRIaGhrExMSQmZnJ48ePcXFxQUtLiwcPHmBlZcXYsWPz6DtW8BX09hGxsbGoq6ujpaXF06dPuXnzJn5+fn/rT92yZUv69+9P7969uXTpErq6upQo8f/Yu8+wqM7t7+PfoYpiARsiotHY0FhiV5qCIhiaYFeMUVFsEXtPrNEES2JBrEQwsYIohGqDGGwYiYhdEFRUVIpipO7nBQ/zP54kxqPAptyf68oVhWH2b+MwzKy97nWr8/jxY/r27cvq1aupUqUKdevWZdOmTfTq1YsDBw6Qnp5OTk4Od+7cwdvbGyic5TtkyJD/KaeqqiqjRo1i7NixpKenY2FhgZGREY0aNaJGjRr06tWLefPmoa6uXizfl549exITE8PmzZtZsGABu3btwtPTk27duhXL/Qv/rlq1ahw9epRRo0ZhamrKL7/8Qo8ePeSOJXwASZKYM2cOP/zwA4cPH8be3v6dv9be3p7ly5eTlJT0xkaqgryK9hgYPXo0f/75JwsWLCA4OJg5c+bQtm1b8f5PECoh0SksCJWUs7MzU6dOZd++fTx9+pSVK1diZGTE6NGj33hBUDTfUig958+fJz09HSsrq3+9rUKhYMmSJaxbt47PP/+cjRs3lkJCobhJksTs2bNZs2YN/v7+oiBcDty4cQMbGxscHR357LPPuHXrFp988slbZz97eHigoqLC0qVLMTc35969eyQmJrJixQoaNmxIQUHBvz7nampqMmLECHbu3Im+vj7r1q2jc+fOmJiYEB0dzSeffAIUdnoGBwfj5OREQEAAZmZmqKuro1AouHLlCo8fP6Zx48bcuHGD8ePHs3btWrZt28bmzZtJTEwECi9QAZVqA6myOj7i1KlT6Onp0a1bN4YPH67sis3KyqJr164kJiayfft2kpKSAGjTpg0Affr04cyZM8qxEi9fvuTXX3/l/v37QOFzz+nTp2nTpg0bNmygc+fOtGvXjsDAQD799NMPyty1a1eCg4Np0KABx48fZ9euXcyfP59OnTrh6enJlClTirUrW01NjS+//JLr16/TqlUrevTowYQJE5TnLpQ8TU1N5UVNS0tLwsLC5I4kvKf8/Hzc3NzYsmULQUFB/1NBGApnfxsYGIiVbGVE0e9xdXV15cU4Jycn7t+/T1paGo6OjnLGEwRBTpIk/et/hTcTBKGiiouLkzw9PaUVK1ZIU6ZMkbZs2SJdu3ZNun//vtzRKqXFixdLnTt3/p+/bs+ePZKqqqq0dOlSqaCgoASSCSUhPz9fmjhxolS1alUpIiJC7jjCv8jIyJBmzZolqampSRYWFlJcXNw7f+39+/elJ0+evPGx/Px8KT8//72yXLhwQfrtt9+Uf9+8ebM0atQoKTExUXJ1dZW++OILKTs7WzIzM5MOHz6svJ27u7s0d+7cv9zf5cuXJV1d3TeeP65fvy61bNlS8vX1lfLy8t4rZ3kyceJEadCgQXLHkLKyspR/zsvLk5ydnaXvv/9ekiRJcnBwkKZMmSJJkiQNGzZMql27ttSsWTPJ2NhYsrOzkw4cOKD82uTkZMnMzEyKjo5Wfsza2lqysbGRhgwZItWrV0+aOXPmG8crCenp6dL58+el0NBQ6cSJE9KVK1dK9HiSJEkRERFSy5Ytpdq1a0s7d+58758z4X9XUFAgzZ07V1JXV5cOHjwodxzhf5STkyMNHz5cqlmzpnTmzJn3vp9JkyZJlpaWxZhMeF9z5syRFAqFNG3aNOnEiRPK3+c3btyQtm7dKklS4b+7IAgVy/+v5b613lv2WiEEQShVz5494/vvvyc3N5eUlBTi4uI4cuQIS5YswczMjGfPnrFt2zaaN28ud9RKIzg4GGtr6//560aNGkXNmjUZPHgwz58/Z926df84k1goG/Ly8vjiiy8ICAggLCyMXr16yR1J+AcFBQX4+Pgwd+5ctLS02L9/P46Ojm/tDP5vReMhirqBVVRUPuhntHPnzm/83dbWFj8/Pz7//HN0dHTw8PAAoF69em+Mpjh27NgbGwAdO3YMf39/Hj16xIQJE5TndOjQIbZv387w4cMZOnSosqtz//79pKamMnToUOrUqfPe+cuisjA+Ij8/n9atW+Pn50enTp1IT0+nVq1aylnRgwYNYvfu3QDY2dmRmZlJYGAgAJs2bWLjxo3Y2dmhqamJgYEBhoaGnDt3jnbt2lG1alW2b99OYGAgly5dwsfHh379+pX4OdWsWZMuXbqU+HH+k4WFBbGxsaxbt44pU6awc+dOtmzZQvv27Us1R2WkUChYvXo1urq6DBkyhK1btzJ+/Hi5Ywnv4PXr1wwdOpQzZ85w8uTJD9pfxMHBARsbG+VzmCCfNWvW8Mcff+Dp6cn58+d59OgRPXr0ICYmBjc3N4BiG+cjCEI5829VY0l0CgtChebr6ys5OTlJ165dkyRJkq5duyY5OTlJYWFh0q1bt6Q1a9ZIV69elTll5fH48WMJ+KDOjBMnTkja2trS6NGjpdzc3GJMJxSn7OxsydnZWdLV1ZUuXrwodxzhLc6fPy9169ZN0tLSkpYtWya9evVK7kj/KD8/X4qNjZVevnyp/NiiRYukSZMmSQkJCdLSpUslExMTSZIkKTMzU5IkSTp79qzUuXNnydPTU/mcsWDBAsnQ0FAaO3bsG/d/+vRpaeDAgdKnn34q6enpSZaWllJsbGwpnV3JGzNmjDRq1KhSPeZ/d+mGhIRIjo6Oys7WjIwMKTExUfn5yMhIqV27dpIkSdLTp0+lW7duSZJU2J158+ZNycLCQoqMjFTefteuXZKFhUWlXv2TkJAg2dnZSaqqqtL06dOljIwMuSNVGtu2bZNUVFSkNWvWyB1F+BcvX76ULC0tpYYNGyrfF3yI7OxsqUaNGtLevXuLIZ3wvop+lyQlJUkzZ85UvkbYvHmzpKWlJTVp0kQyNjaW0tLSZE4qCEJx4x06hUULmSBUcuHh4fTu3ZtWrVoBhReKXr58SVZWFh9//DFz5szByMhI5pSVR1hYGDo6OnTt2vW976N3796cPHmSwMBAnJ2dP3h3d6H4vX79moEDBxIVFcXp06fp1KmT3JGEv/H48WPGjh1Lt27daNy4MdevX2fx4sVoaWnJHe0fqaio0K5dO6pVq6b82MCBA/njjz8YOXIkSUlJbNq0iTt37nDgwAFev35Nt27d+Oqrr/D09ERNTY2ffvqJb775hi5dupCWlsaiRYuUm9Ns2LCB4cOHExMTQ0pKClOnTiU/Px9A+f/yLC8vr1jn3P4dSZJITU3F1dUVXV1dJk6cSEhIiPLzT548ITc3l8zMTJKTk6lRowaNGzdWfv7IkSMMGDAAAG1tberXr09qaioKhYKkpCSeP39O3bp1lbcfPHgws2bNQl9fv0TP620KCgqUM6rl0KRJEwICAvD39+fIkSO0atWKffv2FTW/CCVo/Pjx7Nu3j0WLFjFv3jzxPS+j0tPT6devH3fv3iUqKkr5vuBDaGhoYGNjQ0BAQDEkFN6HJEnKFUmNGjWiW7dufP755xQUFNC3b186duxIQkICs2fPFt3cglBJiaKwIFRyjo6ObNq0iV9++YX79+8riwRFO5mLF++lKyQkhL59+37w8uXOnTsTFRVFTEwMNjY2vHjxopgSCh/q5cuXDBgwgNjYWCIjI8VOz2VQbm4u69evp0WLFly4cIETJ06wf//+cruDeseOHfHz82PVqlVs27aNdu3a8fr1a3x8fHjw4AFQeEGqT58+XL58mWPHjuHu7o6Pjw+urq4EBQURFRXFnTt3CAwMJCAggBMnTgCF4wuKlhcXFVN37NiBt7e3rEXA91WS4yMSExMJCQlBoVDg7+9PUlISR48epVGjRkyYMEF5u4iICG7fvs2IESMYNWoUy5Yt4/Hjx8rP37x5U/lYlCSJffv2MXHiRM6dO8euXbvo1avXGwWdatWq0b9///9p1Elx27p1KzExMbIdv4itrS1Xr17liy++wMXFBUtLS65fvy53rApv0KBBBAYGsnHjRiZMmFAhLiBVJKmpqfTu3Zv09HSioqL46KOPiu2+7e3tCQ4OJjs7u9juU3h3CoWCy5cvK/8+aNAg3NzcGD9+PA4ODnz22WdA4e9y8Z5PEConURQWhErO3t6ewYMHs337dmxsbDhy5Ahffvkl2traPH/+nISEBOVtxYuFklVQUEBoaOh7zRP+O61bt1buMm9hYcHTp0+L5X6F95eRkYGVlRWJiYlERUXRokULuSMJ/yU8PJz27duzfPlyVq1axaVLlzA3Ny/240iSREFBgbIDt6TVrVsXU1NTZcdQmzZtMDIyon///kyYMIHg4GBGjhyJpqYmOTk5fPHFF2hpaWFlZUWDBg04d+4cz58/Z8qUKbRv355x48axcuXKvxxn9erVuLm58eTJkzfOtbzIy8sr1qJwWloa33//Pd27d6dNmzaMHDkSgKNHjzJz5kyMjY2ZOXMmurq6nDlzBoA7d+5Qp04dvvvuO5ycnLh06RJbtmwB4Pr169y8eZOJEycCkJ2dTf/+/TEyMmLs2LHUrFmThQsXFlv+4pCXl8eiRYu4ceOG3FEAqFq1KitWrODKlSvKzvqFCxfy6tUruaNVaP369SMiIoKDBw8ybNgwUSQsI+7fv6/83XD69OliX1FgbW3N69evOXXqVLHer/BuLl26hK2tLYcPH1Z+bPz48fTu3ZucnBzGjBkDFL4HkfPCoSAI8hEbzQmCwNKlS7l//z7q6urUr19f+fGMjAwCAgJISUlh9uzZbyxHFYpfTEwMT58+xcrKqtjus3HjxkRFRdG/f39MTU0JCwvDwMCg2O5feHdF/7Z//vknUVFRsi7lFv7q7t27zJw5k6NHj+Lq6sry5ctLdBM1hULB8uXLuXXrFj4+PiV2nLfZsmUL58+f548//uDrr7+mQYMG3Lt3j7i4ONq0aQPA1atXSUxMZO7cuXTp0oUOHTqgrq6Ovr4+fn5+pKWloaOjQ2xsLAcPHmTPnj3Y2trSv39/ZXFVkiQUCgX79+9HR0cHCwuLEh/R8L6KY3yEJEmkp6eTlJRE//796dKlCwsXLqRmzZqEhYUB4O7uTrdu3QC4d+8eenp6NGrUiEePHqGtrU3Xrl0xMjLCyMiI6tWrc/bsWQD27dtHhw4d2LBhA3v27EFfX5/du3ezbNkyli9f/mEnX0LOnj1Lenp6sV3wLC4tW7YkLCyMgwcP4u7uzt69e/nhhx+ws7OTO1qF1aNHDyIjI+nXrx92dnb4+fm9MepGKF137tzB0tISAwMDAgMDqVmzZrEfo2bNmpibmxMQEFCsr2+Fd/Pjjz/SpEkTfv75Z2JiYliwYAHa2toMGjSIxo0bo6en98aICUEQKh/x0y8IAgANGjR4oyAMoKury/379/Hw8MDQ0JDExER5wlUSwcHBdOjQgQYNGhTr/davX5+TJ09Su3ZtjI2NuXXrVrHev/DvHj16hLm5OQUFBSXSiSO8v6ysLBYvXoyRkRFPnz7l4sWLeHp6lmhBuEjz5s05evSorB1zXbt2Zdy4ccrnnYYNGzJo0CB69uzJrFmzmD9/Pt27dycvL4+TJ0+iqqpKQUEBzZo14+nTpygUCiIjI5k1axaGhoZMmzaNLl26vPH9e/nyJZs3b2bMmDGcP39eWXRNS0t7YyxCWfAh4yNCQkJwdnamVq1aypUA9+/f5+jRo9ja2ip3fAcwNzdHW1tbecw7d+7QqFEj9PT0qFq1Kvr6+mRlZQFw69Yt8vPzkSSJM2fOcPDgQX777TfmzZtHYGAgdevWLdMdXoGBgXTv3r1MXlhWKBQMHjyY69ev4+TkxMCBA7Gzs3tjlZRQvD755BPOnDnD7du36du3L8+fP5c7UqUUHx+PiYkJLVq0IDQ0tEQKwkUcHBw4evRouVo1UlGEhoayc+dOvLy8yMzM5NChQ0Dhe7z+/fsDlOnfH4IglDxRFBYEAfi/WZAvXrxg/fr1tG3bFl1dXaKjozl48CBZWVk0adJE3pAVXEhIiPIFWnGrVasWoaGhGBkZYWJiQmxsbIkcR/irpKQkTExM0NbW5sSJE2WyMFIZSZLE/v37adWqFbt372b37t1ERkYq5+OWBhsbG/7880/lfN6yQE1NjRUrVuDu7k5aWhqjR4/Gy8uLixcv8tNPP6GiokJycjJHjhxBQ0OD33//nTFjxuDl5YWrqyvJyck0btxYWWC4fv067u7u+Pr6YmVlRd++fQE4ePAgI0eOpGXLlgwbNqzUxmj8m759+zJjxox3vn3RbNR169YxduxYzMzMuHbtGnZ2dmhpaaGqqqo8twYNGig38ikqrgP89NNP2NvbK2cw29nZERERga+vLzExMZw6dYqePXuiUChYt24df/75JwcOHGDw4MHFeeolJjAwULkxXllVvXp11q5dy6VLl3j+/DlGRkasXLlSjDgoIU2bNuXXX3/lxYsXmJmZkZKSInekSiUmJgZTU1O6d+/O0aNHqVq1aokez87OjgcPHpSJueKVyZUrV8jMzKRFixbUrl2bwYMHs3XrVoYMGcL58+fljicIQhkhisKCIABw/PhxGjZsSN26dfn555+ZPn062dnZ/Pbbbzg5OYllRSXs+fPnnDt3rkSX11atWpUjR45gbm6OmZmZcn6lUHLu3LmDiYkJDRs2JDw8HB0dHbkjCUBsbCzm5ua4uLjg4uLC9evXGTZsWKl3y9SsWRNLS0v8/f1L9bjvYtCgQezcuRMnJyfU1dUxMTHh+PHj9O/fn4kTJxIbG8uUKVO4ffs2CQkJfP3113h4eJCRkUH//v2pVq0aV65cwdnZmXbt2uHq6kqbNm1o3bo1kZGR/PDDD5iampKeno6uri5RUVGA/POHXV1dadSo0b/e7uHDhyxdupSIiAgA9PT06NixI1OnTkVfX/+NzT3V1dWBwjEKHTp0AP5vR/jc3FyuXr1Kt27dlLcbMWIEU6ZM4bfffmP8+PEMGDCAoUOHAoVdlpqamsV6ziUpMTGRq1evKjczKuvatWtHZGQknp6ebNiwgXbt2in/jYXi1aBBA06fPo22tjbGxsbcvXtX7kiVQlRUFH369MHGxoYDBw6UyvOJgYEBnTp14siRIyV+LOH/xMXF8cUXXwCFFzBNTU05e/YsXbp0YePGjfj5+cmcUBCEskBUeQRBAAo7SWfPnk1GRgbnz59n3LhxqKmpkZ+fr1y2mpiYqNypXihe4eHhaGtr06NHjxI9joaGBnv37mXYsGH07duXkJCQEj1eZVa0NNPIyIhffvmF6tWryx2p0nv27BmTJ0/m008/pVatWsTHx7Ny5UrlMn45ODo6EhAQoOw4Lat69uzJxYsXsbe3Z8iQIfj6+mJra8v48eN5+vQpZmZmbN26lT179rBjxw6WL1/OsmXL8Pb25ssvvyQ1NZW6detSrVo1jh07hqmpqbIj9+XLl7x+/Rr4v87bw4cPs3bt2lLvIFRXV3+niwOpqal8++23yrFKffv25cGDByxYsAAjIyPGjBnzl42VNDQ0uHjxIvB/53n58mWePXtGp06dmDdvHs2bN+fJkyeYm5vj6enJpUuXWLBgAVpaWsV6nqUlKCgIAwMD2rVrJ3eUd6aiosLnn3/OjRs36N27N/369WPo0KHi9U8J0NXVJSIigmbNmmFsbExcXJzckSq0sLAwrKysGDlyJN7e3sW6qea/sbe3JyAgoNSOJ0D//v2Vs+YlSSInJweAcePG0aNHDyZNmsRXX30lZ0RBEMoAURQWBAGATp06MX36dDQ1NcnLy1O+YVVVVUVFRQWFQsEPP/zAnDlzZE5aMQUHB2NpaansFCtJqqqqbNmyhenTp2NnZ8f+/ftL/JiVzeXLlzEzM6N79+4cOXKkxJdmCm+Xn5+Pp6cnLVq0ICIigqCgIAICAmjWrJnc0bCzsyM1NZXffvtN7ij/SldXFzc3Nz7//HN0dHQoKCggPz8fXV1dxo4di6WlJTt27KBt27ZcvHiRkJAQfHx88PHx4cWLF9ja2nL37l0yMzMxMTFBXV2dZ8+e0bx5c+VqFDU1Nfbt28esWbPQ0NBQFi22bt1a6h2bBQUFHD16FB8fH9LT09/4XGxsLEZGRty6dYv79++joqJC3bp1OXfuHN7e3nTs2JGVK1cqL7wVFBTw8ccfKy8OFZ2Xv78/ly5dwtTUlNTUVLZv307Dhg2RJKlCPG8UjY4ojzMrdXV12bp1K2fPnuXWrVu0atWK9evXK8d8CMWj6EJRr169lJ2MQvHz9/fH1taWadOmsWnTplJfAWhvb09cXJzoCC8l+fn5JCYm4uvry/Xr11FTU0NDQwMobASaNGkS8fHxTJw4UeakgiDIrfQuDwqCUObl5eWhoqKCmpoakiRRUFCAiooKeXl5qKurM378eLErdwkoKCggJCSkVHeOVygUrFq1Ch0dHYYPH05GRgaurq6ldvyK7OzZs1hbW2NjY4O3t3epFPqFfxYZGcm0adO4e/cuS5YsYdq0aco3RmVB/fr1MTY2xt/fHxMTE7nj/E/+s6iQlJRE9erVMTU15eOPP8bGxoabN2+ybds25s2bx8uXL9HT06Nu3bpoa2vTotfplh0AACAASURBVEULoHC8wKtXr6hbty6vXr1i3bp17Nixg59++omePXsq5+6mp6czbdo0srOzsba2xtXVtcS7Tz08PIiJiUFLS4sff/wRDw8P5fiHCxcu0L59e/T09Lh06RJ9+/bF2dmZLl260LFjR9q2bcurV684efIklpaWqKmpkZycrNyALz8/H1VVVXr06EFoaKhy1nKR8lhE/W9ZWVmcPHlSubFRedW1a1fOnz/P1q1bWbhwId7e3mzZsoVevXrJHa3C0NTUZN++fUycOBELCwuOHDnyl58J4f35+PgwZswYli9fzvz582XJ8Mknn9CkSRMCAgJwd3eXJUNlsm3bNnbu3MmTJ094/fo1jo6OeHh4KC9M5uXloaurK3NKQRDKAtEpLAiCkpqaGioqKuTk5KBQKJTzDouKWq1bt6Z69erloqOtPImNjeXx48cltsnc28yePRsvLy/c3NxYs2ZNqR+/ojl16pSyOLRnzx5REC5hb5s/m5yczLBhwzA3N6djx47cvHlT2X1a1jg6OuLn5yf7PN0PYWhoyHfffcdHH30EFL7hbNGiBR4eHsyePRs3Nzf09fWpW7cuqampNG3aFIAbN26gUCho1aoV06ZNY8OGDdSuXRs/Pz+Sk5OVhed58+YRHx9PaGgomZmZdOjQoUQ3yomPjyc6OppFixbh5eVFixYtlKsqHj58yOPHj5kwYQKamprcvXsXLS0tXF1d6dixIzk5OVStWpXHjx/z8uVLZYH39evXnDx5Evi/grqtrW2FLX4dP34chUJBnz595I7ywVRVVZk8eTI3btygQ4cOmJiY8MUXX5Camip3tApDVVWVbdu2MWXKFAYMGFDuLyaUFVu2bOHzzz9nw4YNshWEofBClxghUXrWrl3L9OnTSUpK4uzZs6SkpLBnzx7l58+dO0d4eLiMCQVBKCtEUVgQhDfMmDGD+fPns2TJEvr168fUqVPx8PBg/PjxxMXFUatWLXbt2iV3zAolJCSENm3avNPmRiVh3Lhx7N+/n8WLFzN37txyXZiSU0hICNbW1owdO5Zt27ahqqoqd6QK6ffff8fT0xP4+25KSZL45ptvaNWqFXfu3CE6Oprdu3ejp6dX2lHfmaOjI/fu3ePy5ctyR/lgRY/7ovEI169f59mzZzg4OODo6MidO3eIj48HCjfBiY6Opnnz5ly6dAkfHx9WrlzJhg0buH//Pj/++CNQuJqiaOZwUbF58uTJdO3aVXncS5cuKR8XH6Lo+e/169cYGxsrN3bLzc2lY8eOAFSpUoXU1FS6dOlCp06duHTpEnPmzMHb25vAwEAePXoEQGZmJoaGhsrvyZIlS/Dx8QEqRifwvwkMDKRPnz4VYgxGkfr16/Pjjz9y8uRJzp07R8uWLfHy8lJ2tAsfRqFQsGbNGpYvX86QIUPYsWOH3JHKtTVr1jBt2jR27tzJlClT5I6Dg4MDUVFRPHv2TO4oFVp2djYNGzZEoVAgSRJNmzZl4sSJeHl5KTd1nT17tnImviAIlZsoCguC8IY+ffpgYGAAgIuLC46OjqSlpdGqVSt++uknMjMz0dHRkTllxRIcHIy1tbWsGZydnQkKCmLTpk1MmDChzG96Vdb4+/tjZ2fHjBkzWL9+faUo+JS2tLQ0XFxcsLW1fevmOHl5eRQUFLBp0ybOnj1Lt27dSjHl+2nSpAkdO3bE399f7ijFrlWrVixfvpxOnToB4OTkRMuWLZk/fz4TJ04kOzubYcOGERERwdChQ5kwYQImJibY29vj5eUFFHbVVqlShdzcXAYPHoyhoSFLly5VHmPVqlXMmTOHhISEN479dxe4MjMzWbJkCd99953yDfF/FvSKfnY//fRTxo4dC8DGjRvZuXMn+fn5ZGdnc/ToUerXr8+KFStwcXEhICCAx48f0717d4KDgxk5ciQNGjQgLS2N0aNHK++7Vq1a1KpVqxi+q2WfJEkEBQXx2WefyR2lRJiZmXH58mUWLFjAzJkz6dGjBzExMXLHqjDmzp3L1q1bmTBhAt9++63cccodSZJYuHAhixcvZt++fXz++edyRwLA2NiYWrVqERgYKHeUCk1TU5Pp06cTFRWl/P1mY2PDxIkTGTduHE+ePOHhw4cMHTpU5qSCIJQFinfpCFMoFJLoHBOEykGSpH8taBXNGhY+XEZGBrVr1yY0NBQLCwu54xAdHY2NjQ19+/bFx8cHTU1NuSOVeT/99BMuLi4sW7aMBQsWyB2nQsrKysLGxob8/Hx+/fXXf739uzyPlTXLly9n//79xMXFyR2lxIWGhvLzzz8zYMAA+vbtS61atejTpw9OTk5MnjwZgEWLFnH58mVl8eDGjRt4e3uTmpr6RvfgnDlzCA0N5ejRozRu3BiAnJwcfv/9dzp06PDGc1hBQQGLFi3iwoULDBo0CC8vL4KDg6lXr95b8/7yyy88evSI4OBgNDQ0cHV1xcrKitGjRzN8+HDu3btHcnIyCxcuBCAgIIAGDRq80clc2fz+++98+umn3Lt3D0NDQ7njlKjk5GTc3d3x9/fHzc2NFStWVJrif0k7ePAgI0aMYMaMGXzzzTfl7nldDgUFBUyfPp3t27fj5+cne9PBf3NxceHly5f4+fnJHaVCy8jI4Ny5c7Rr1+6NlVLTp0/Hz8+PJk2aEBkZKWNCQRBKw/9fMfDWX56iKCwIwj/Ky8tTbjqnUChISEjA19eXrKwsbG1txSYrxeDw4cOMHj2aZ8+elZkC7JUrV+jXrx/t2rXDz8+PatWqyR2pzNqxYwcTJkxg3bp1fPnll3LHqXDS0tLQ0dEhJycHDw8P2rdvz4ABAwgNDUVNTY02bdqgp6dXIS5UXb16lbZt23Lz5k2aN28ud5xSFxoaire3N1OnTuX169cMGTKE/fv3K+fRDhs2jMaNG+Pu7k79+vVJTEzEw8ODLVu2YGJiwrhx43B2dqZKlSqcPXuWM2fOMGPGjDceF1lZWRgYGHD27FlatmzJ6tWr0dLSwsnJCQMDg3+9mHDv3j06duxIQkICNWvWVH781KlTPHz4kAEDBrzx8cps+fLlHDx4kD/++EPuKKUmJCSEKVOmULt2baKjo//xOSkuLo7r16/TuHFjunTpUsopy5/Q0FAGDhzIyJEj2bJlixjN9BZ5eXmMHz+ew4cPExgYiKmpqdyR/uLw4cO4uLjw9OlTtLS05I5TaRRtbnrjxg369+/PV199VWY6yAVBKDnvUhQu3++gBEEoMTk5Ocol2kVvkj/66CMGDRpETk4OLi4ucsarMEJCQujTp0+ZKQhD4Q7Rv/76K7du3aJv376kpaXJHalM+v7775kwYQJbt24VBeFiFhMTQ9euXbG3t2fTpk3k5+djYGBAQEAA5ubmjBs3jo0bNzJgwAAeP36MiopKuZ+FbWRkRPPmzSvkCIl3YWpqipGRES4uLqxevZply5bRp08f8vLyOHbsGHfv3mXWrFnUr18fKLwgc+nSJRYvXsysWbPw9vbm1KlTKBQKWrduzYQJE/5SlMvKysLMzIz79+8DYG1tTXp6+ls3rAsMDOTatWtA4QZzNjY25ObmAij/b25uzvDhw0VB+D9U5NER/6R///7ExcWxd+9e8vLy/vL5goICDhw4QK9evfD398fS0lJspvYOrKysiIiI4MCBAwwbNoycnBy5I5VJOTk5DBs2jKNHj3L8+PEyWRCGwn/P/Px8jh8/LneUCun+/fvExMTw/PlzsrOzlR8vupjSsmVLdu3axaBBg+SKKAhCGSOKwoIg/EVSUhLjx4/Hw8OD58+fvzFvsVWrVqxbt460tLR3WsYt/DNJksrEPOG/06xZM3799VcyMzMxMzNTbpwkFFq1ahUzZ85kz549jB8/Xu44FUpGRgYTJ07ExsaGESNGEBwczM6dO/nss8+IjIykc+fOJCcn4+/vT4sWLRg1apTckYuFQqHA0dGx0i6p1dLSYvHixdy+fZuDBw/i5uYGFBZlvby8cHNzo06dOkDhXODLly8zffp0li5diq2tLTVr1iQ8PJz8/Hxq1aqFtrY28Oa84GrVqqGnp6d8PmvdujX6+vpcu3aN3NzcN7qEiy4y/PHHH3z55ZfY2toyZMgQTExMlDnU1dVL/htTDj1+/Jjz589XuqIwFG5C+PHHH6OhofGXz4WGhhIcHMyKFSvYu3cvISEhXLx4UYaU5U+PHj2IjIwkKioKW1tbsrKy5I5Uprx69Qp7e3t+/fVXTp8+XaY70LW1tbGwsCAgIEDuKBXSkSNHsLGxYe7cuezdu5eLFy/y5MmTNy6m9O7dW6wCFARBSRSFBUH4C0NDQ8LCwtixYwfW1tYMGzaMHTt2cO7cOQoKCvjxxx9p0qQJNWrUkDtquXb16lUePHhA//795Y7yt/T19Tl9+jRaWloYGxv/ZROnykiSJBYsWMDXX3+tnHUoFK/o6GgkSeLrr79m3LhxmJubk5iYiK6uLm5ubjg6OgKFRdQVK1bwxx9/kJCQUCFmTQ4cOJBz587x4MEDuaPI6j87btu1a4erqysODg7Kj2VlZVGzZk1lUfbly5cYGxuTnp6OqqqqcgxETEzMX4rCDRs25MGDB6Snp6OhoUHdunV5+fKlsnu4SFFReM6cOWzevBkrKytOnz7NhAkTSvLUK4Tg4GB0dXXLxSaPpSUjI4Njx47RrVs35QWPqKgoZTd7eV/pUBrEKqa/l5mZibW1NfHx8URFRdG2bVu5I/0rBwcHjh079sbzs1A8pkyZQqtWrXj48CHBwcHMnDmTGTNm4OnpSXR0NElJSX+7kkEQhMpLFIUFQfhbtra2jBgxgrCwMMzNzdm3bx9LliyhTp06LF++nFGjRtGuXTu5Y5ZrwcHBtGzZko8++kjuKP+odu3aHD9+nCZNmmBsbMzVq1fljiQbSZKYPn0669ev5+jRo8ripPBhfH19cXBwYMmSJTx+/JhevXoxYcIEcnJyUFVVRUNDQ9lN9+WXX9KrVy9lx0tSUhLt27enevXqcp5CsenSpQv6+vocOXJE7ihlRtOmTbGzs1Nu3CVJEg0aNKB58+acO3cOAG9vb6Kjo5UX2JKSkli5ciVffPHFXy4W2NnZcf/+faKjowGoWrUq0dHRfPTRRzx79oykpCQAZbFOTU2N5s2bM2XKlDL9XF2WBAUFYW1tLWa//oeQkBBq1qxJ7969UVNTIzk5mZycHIyNjQEqxEWt0lC0iunFixeYmZmRkpIidyRZPXv2DAsLCx49esSvv/7Kxx9/LHekd2Jra8uTJ0+Uz+FC8Sgqsn/77beMHTuWgwcP4uvrS58+fbhw4QILFizAzc1NdNoLgvAGURQWBOFvjRw5Em9vb2rWrImbmxsRERF4enoSHx9PbGws7u7uBAcHc+vWLbmjllshISFltkv4P2lraxMUFES3bt0wNTV96/zNiio/Px9XV1d27dpVbv7dyoNdu3bx7bffMmrUKC5cuMCqVat4+vQpn3/+uXL5dZ06dWjRogWSJPHq1SuCgoIYNWoUMTExrFy5kiZNmqCrqyvzmRQPFRUVHBwcKu1c4XdRVDwbOnQoV65cQVdXl4MHDzJkyBAGDx7Mzz//zNixY7lz5w5bt279S2Gybdu2dOzYkYULF3LhwgXu37+vfKzNmTOH2NhY0b32AXJycggNDa2UoyPe5vr169SoUYOWLVsCcPHiRbKzs2nUqNE/fo14HP69olVM1apVw9jYmLt378odSRYpKSmYmZmRm5tLZGTkWx9LZY2enh7dunUTF0CLkSRJyouZbdq0oU2bNgA0atSIL774Al9fXw4ePMjkyZPF/HtBEN4gisKCIPwtU1NT9PX1lbMX8/Pzadq0KXp6elSpUgUonLX41VdfyRmz3Hrx4gVRUVFlcp7w39HU1OTAgQPY29tjYWHBiRMn5I5UavLy8nBxceHQoUOEh4djZmYmd6QK4/r160ybNg0nJyfWrFlDVFQU1apVQ11dXVkQuXr1KtnZ2SgUCqpWrUq/fv1o0KABc+bMoW3btnh5ef1lQ7HybODAgZw6dYrnz5/LHaVMa9WqFUFBQSQlJeHr64uzszO7d+9mxIgRNGrUiF27dtGjRw/gzaX5qqqqjBkzhp49e/Ldd9/h4eHB9OnTAdi5cye2trYV6vFU2qKionj16hVWVlZyRylT9u/fr+w0v3DhArGxsRgYGPDJJ5/849cUPQ4TExNLI2K5oqurS3h4OM2aNcPY2Ji4uDi5I5Wqe/fuYWJiQvXq1Tl58qRyA87yxN7eXswVLkYKhUK5AkZbW1t5ASovL4/8/HwkSaJOnTrY2NiIcTWCILxBvOoVBOEf7d27l+rVq1NQUKDstsrLy1P+2dnZmTNnzsgZsdw6efIkqqqqZXZ36L+jpqbGzp07cXV1xdraulJ0eGRnZzN48GDCwsI4ceIE3bt3lztSubZr1y68vLw4duwYUDgjMiEhgT///JN27dqhoaHBjRs3yM7OVhZE7t69i5OTEwAbN27k0KFDbNiwgZCQEDZs2CDbuZQUU1NTatSoofweCW+nra2t7JBzdHTkm2++4fr16zRq1Ihp06aRkpLyt0vzN23axIYNG7hw4QI2NjalHbvCCgoKwtjYWDnuQyg0cuRI4uLiOHbsGPPmzaNq1arKi8J/V6CJi4tj+/bt9OjRg9GjR/9l5rVQ+LN/7NgxevXqhampKWfPnpU7Uqm4ceMGxsbGNG7cmPDwcHR0dOSO9F7s7e25ceMGN27ckDtKhfDgwQPMzc3R19dn1qxZBAcHA4Wv3Tdv3gygnCUsxtUIgvCfRFFYEIR/ZGhoSLVq1VBRUSErK4vU1FTU1NTIz88HCme76erqEhERIXPS8ic4OJjevXujpaUld5T/iUKhwMPDgyVLluDs7Iy3t7fckUrMq1evcHBw4OzZs5w+fZqOHTvKHalc27FjB6GhoTx79gwHBwciIyMxMTEhMTGRAQMGYGpqSoMGDdi9ezdLly5VjqbJzs7G39+fnj17snfvXlq1agWg3GSsolFXV8fOzk6MkHgPtWrVYu7cuURHRxMWFkbTpk25d+8egPL31n/S19cXO7AXs8DAQDE64m9YWFhw6tQpfH196dmzJ3PmzMHQ0FC5KeJ/KloGnpaWRkpKCqNGjaJevXoyJS/bNDU12bdvH05OTlhYWBAeHi53pBIVGxuLiYkJHTp0ICgoCG1tbbkjvbdWrVrRvHlz0S1cTBo2bEhoaCgFBQX88ccfrF+/nubNm9OjRw+++uorFAoFampqcscUBKEMUrzL8gGFQiGJZQaCUDktXbqUAwcOUL9+fTp06EBcXByqqqr06NEDDQ0NAgICaNeuHV5eXnJHLTckSaJp06a4u7szbdo0ueO8ty1btjB16lTWrl2rXH5dUbx48QJbW1sSExM5fvw4zZo1kztSuZabm4udnR1TpkxhwIABrFixgsuXL3Po0CGys7NxcXGhb9++jBs3jri4ODZv3kz37t1xdnamZs2aGBgYKDe4rAwCAgIYOnQoT58+FUVLody4efMmLVu25Nq1a8qLN8Kb8vPzUVVV5eDBgwwaNOitt12yZAlpaWnMnTsXAwMD5cf/rpBc2UmSxLx581i/fj0//fQTzs7OckcqdmfPnsXa2pr+/fuzZ8+eCnFhdPbs2fz2229i1WExyM3NRV1dnV9++YWTJ0+ybNkytLS06N69O5mZmTx48IBvvvmGSZMmyR1VEIRSpFAokCTprS8aRKewIAhv1aZNGxYsWMCmTZswNzdn8uTJfPfdd1SvXp0qVapgampK1apV5Y5Zrty4cYPExMRyM0/4n0yaNAkfHx9mz57NkiVLKsyMsrS0NPr27cvDhw+JjIwUBeH3kJmZyaJFizh8+DDXr19HXV0dExMTjIyMAKhRowbNmjXjxYsXaGpqUr9+ferUqQNAkyZNuHDhAioqKlSrVo3AwEASExMrTUEYoF+/fqioqBASEiJ3lDJFkqQK8zxTEQUFBdG0aVPlLEvhr1RUVMjMzOTKlSt/Owe3aJZ6bGwsp0+fxsHBAX19/Tdu8+TJEw4cOMDw4cPJzc0tldxlnUKhYM2aNSxfvpwhQ4awc+dOuSMVqxMnTmBpaYmzszO+vr4VoiAM4ODgQHR0NI8fP5Y7SrlX9JgwNTUlMzOTpUuXApCRkcGZM2fIyMj41wtRgiBUTqJTWBCED1JQUMDFixepVasWLVq0kDtOubBhwwY2btzI7du3K0S3T1BQEM7OzowbN47vv/++XG/SlJqaSr9+/cjLyyM8PBw9PT25I5U7UVFRDBkyBFNTU3R1dTl79iwxMTEkJCRgaGhIUlISxsbGtGnThrS0NA4dOsTGjRt5/vw5VlZWXL9+nR07duDj40Pv3r3lPh3ZODs7U6VKFXx9feWOIqs///yTo0eP4ufnR/Xq1Rk3bhzdunV7Y6f14vTfXZiSJHHz5k0uXryIg4OD6Nx+CwsLC9q2bcv3338vd5RyoaCg4B8fw6NHj8bQ0BB3d3d0dXWBwnmgBw4cYMuWLbRu3RojIyPc3d1LM3K5sH37diZOnMjq1auZPXu23HE+2LFjxxg0aBCTJk1i7dq1FeJ1Y5H8/HwaNGjAqlWrGDdunNxxyq3IyEhq1KhBhw4dAHj58iWurq4kJSXx7Nkzrl27JnNCQRDkIjqFBUEoFkWdK5IkKf8MkJOTg4qKCkFBQRVufEBJCg4OxtrausK8sB8wYAChoaHs2bMHFxeXctu59PDhQ8zMzFBRUeHUqVOiIPyeHj16xJIlS9i3bx/r1q2jQYMG/PzzzzRt2hQ1NTUMDAw4c+YM4eHhNGnShF27djF16lTq1avHt99+yy+//MK+ffsqdUEYCjdNCwwMJCcnR+4opa6goIDIyEjGjRuHnp4eY8eORVNTkyFDhtClSxcUCkWxF4QlSSI/P/9v57teuHCBSZMmUb9+fUaNGkVoaKhywx6hUEZGBpGRkWKe8P/gvx/DRXOv/fz8ePLkCc7OzsqC8PHjx7GxscHNzY06deqwffv2NwrCCQkJXLp0qfTCl2Hjx4/n559/ZuHChcyfP79cry7Yt28fAwcOZP78+RWuIAygqqqKra2tmCv8gdzd3Vm9ejXPnj0DCjdh/OGHH6hXrx6urq4A4neWIAj/SBSFBUH4VwqFgoKCAuUb8aIX2BoaGgC4uroSHx8vZ8Ry49WrV5w+fZr+/fvLHaVYmZqacvLkScLCwhg4cCB//vmn3JH+J4mJiZiYmKCjo8OJEyeoXbu23JHKhaJC2vDhw0lNTQXgwoULPHr0CCjcBEhFRYU2bdoov0ahUPDRRx8BMHPmTA4cOEDDhg1Zs2YNR44c4fz58xgbG5f+yZQxAwYMICsri5MnT8odpdTcuHGDRYsW0bRpU8zNzUlKSmLTpk08evSIPXv20LdvX1RVVUvk2AqF4m/vW0VFhZEjR/Lo0SN2795NZmYmtra2GBgY4O7uTkxMTLkuOhWX8PBwNDU1MTU1lTtKuaWqqsrr16/x8PDAycmJjz/+GCgcHbB69WqeP3+uvAB/+PBhANLT09m5cyf29vY4OTnRsWNHUlJSZDuHsmLw4MEcO3aMH374gYkTJ/7tRpNl3Y4dOxgxYgRr1qxRbhRWEdnb2xMREUFWVpbcUcqlO3fucP/+fTp06IC5ublys8U6derg4eHBmDFjAErsd6cgCOWfKAoLgvCviorBL1++JDExEYVCQXZ2Ns+fPwcKd7ytX78+YWFhMict+06dOoUkSRWyC/LTTz8lKiqK2NhYrK2tyczMlDvSO7l58yampqY0adKE0NBQatasKXekckOSJFRVVTlx4gRz5swBYPLkyQQGBjJ37lxmzpxJfHw83t7eDB8+HCgcL7Ft2zaSk5PZv38/gwcPVs7CMzQ0lO1cyppatWphYWGBv7+/3FFK1NOnT9m0aRPdunWjVatWHD16lMmTJ5OcnExYWBijRo1CW1tb7phoaWkxaNAgAgICSElJ4auvvuL8+fN07twZIyMjVq5cSUJCgtwxZRMYGEi/fv3Q1NSUO0q59uTJE0xNTbGwsFCOKjl8+DCmpqYcOHCApUuX0r59e3bu3KkcodKjRw927NhBQkICenp6eHt7y3sSZYSVlRXh4eHK+cvladXF+vXrmTBhAp6ensyYMUPuOCXK0tIShUIh3kO8p7179+Lg4MC8efNYunQp+/btUxaGmzZtSo0aNQAq7EUFQRA+nCgKC4LwTqysrDA1NcXDwwNLS0u6du3K8uXLsbOzY8WKFWhoaIg5gu8gJCQEU1PTCjuXsmXLlvz666+kpKTQu3dvZfdoWRUXF4epqSnt2rUjMDCwTBSfyouigkRmZiY6OjocPXqU2NhYGjduzPbt24HCURKXLl1i/fr1nD9/nqCgILS1tTlz5gx2dnbcvXuXadOmyXwmZZejoyNHjhwpl11ub/P69WsOHTqEvb09DRo0YOXKlRgbG3P58mX++OMPZs+eTcOGDeWO+Y9q166Nm5sbZ86c4c6dOwwfPpw9e/bQtGlTTExM8PLyUl40rQwKCgr45ZdfxOiIYmBoaMjq1auVqylycnKQJIk6derQtGlTABo3boympibPnz+nRo0aGBkZ0bVrV6Cw8PPq1SugsIPw/Pnz8pxIGdGzZ09Onz5NZGQkdnZ2Zb4bVZIkNm3axJw5c/D19VUu/a/IqlatSr9+/cQIife0b98+7OzsgMJNaj/99FOWLVvGd999R1ZWVrne50MQhNIhNpoTBOGdXLhwgSpVqvD48WMUCgVt27YlPj6elJQUdHV18ff3x8jIiC+//FLuqGVa8+bNcXNzq/CdH0+ePMHa2pqsrCzCw8Np1KiR3JH+IiYmhn79+tGnTx/27t2rHIci/LPLly8rx0EULUVMTU1l5cqV6OjoEBERQVRUFACDBg1i2rRpmJiYADBixAiqVq2qLBinpKTQoEEDeU6knHj06BH6+vpEg1vylAAAIABJREFURUXRq1cvueN8EEmSOHPmDD4+Phw4cIDs7GwcHR1xcXHBwsICNTU1uSN+EEmSuHjxIr6+vvz888+kp6czYMAARo4cyYABA6hSpYrcEUvMuXPn6N69OykpKWIWewlYsmQJ6urqLF68mKioKI4dO8aLFy/w9PR843Zr1qwhMjISd3d3TE1NmTp1KmFhYejp6eHp6anchKoyunPnDn379kVPT4+goCB0dHTkjvS38vPzefr0KVeuXMHS0lLuOKXG29ubWbNm8ejRo3L/u6A0vXr1ihkzZrB169Y3Ph4bG8v333/PixcvGD9+PP369ZMpoSAIcnuXjeZEUVgQhGKRmZmpXKIk/L3bt2/TvHlzrl69ipGRkdxxSlzR7M2EhATCw8Np2bKl3JGUzpw5g42NDQ4ODuzcuVO8CfkXkiTx1Vdf8dtvv5GdnU3btm2ZM2cOH330EUlJSVhZWXH16lWMjY1ZtmwZlpaWfP311/j5+REUFMS1a9eYOnUqK1asYNCgQXKfTrlibGxM9+7d8fDwkDvKe7l9+zY+Pj74+vqSkJBA7969cXFxYeDAgVSvXl3ueCUiLy+P8PBwfH198ff3R0NDg0GDBjFy5EhMTEwqXOfW4sWLCQkJ4cKFC3JHqZCuXbuGk5MTmpqaFBQUYGVlhZubGx999BFPnz4lOjqavXv3Ehsby8aNG7G0tGT69OloaWmxYMECtmzZwsaNG1mzZg0jRoyQ+3Rk8/DhQ/r164eKigqhoaFl9qJkQUFBhXuO+DdPnz6lfv36nDx5Uswl/x9IkkRWVtbfrnJ7+PAhP/zwAxEREURHRyvHdAmCULm8S1G4cv3GEQThgxRdHMrPz39jY507d+4wffp0nJ2dxSy7twgJCcHQ0JDWrVvLHaVU1KhRg5CQENq3b4+JiUmZ2Rn9+PHj9OvXj+HDh7N7925REH4Hjx8/5vbt2+zfvx9PT09SUlJITk4GCjuFe/TogYqKCvPnz8fZ2RlDQ0PGjh2LhoYGw4YNw9nZmc8++0wUhN+Do6Mjfn5+5Wozs+fPn+Pp6UnPnj1p3rw5hw4dwtXVlXv37nH8+HFGjx5dJgvCRd/jD/1eq6mpYW1tzd69e3n8+DEbN24kOTmZPn360KRJE+bPn8/Vq1eLI3KZEBgYKEZHlKDWrVsTHx/PsmXL8Pb2ZtWqVbx+/ZqVK1fSrVs31q5dS69evQgNDcXS0pKgoCB++OEHNDQ0qF69OnPnziU+Ph5jY2Nu3779lw7jykJfX5/IyEiqVauGsbExd+/eLfUMhw8f5sqVK2+9TWUrCEPhpmi9evXiyJEjckcpVxQKBSdPniQ8PJyHDx/y5MkT5fgYfX19Vq9eTUBAgCgIC4LwVqJTWBCEdyJJEgqFgrS0tDeW3RV9PDc3F39/f8aPH09GRoaMScuuzz77jIYNG+Ll5SV3lFKVm5vLmDFjOHbsGGfOnKFt27ayZQkKCsLJyYnJkyfj4eEhNt54i/8c73D37l3Gjx/PoUOH0NHRwd7eHisrKyZNmsTly5eZO3cu9vb2rFu3jvz8fBwdHVm3bh3Pnj0jJSWFevXqUa9ePZnPqHy6e/cuzZo14/Lly7Rv317uOP8oOzubX375BR8fHwIDA9HR0WHYsGG4uLjQsWPHcvGz9urVK06dOkXXrl2pU6dOsd//o0eP2LdvH76+vsTExNChQwdGjhzJsGHD0NfXL/bjlYYHDx5gYGDAhQsX6Ny5s9xxKoWbN2/Sp08fXr16xZEjR5SdlUUdpocPH+bBgwdERESQnJzMwYMH+fjjjwGIj4/HwcGBunXrsn79euUs4srk5cuXDBw4kLi4OMLCwkrtNcmjR4+ws7PDy8uLjh07vvG5otfSldnatWvZsmULt2/frvTfi3eRlZXFqFGjaN68OfHx8aioqGBgYEDr1q2xsLCoNA0ogiC83bt0CiNJ0r/+V3gzQRAqu4SEBMnQ0FBydXWVAgMDpdevXys/V1BQIEmSJNWrV086fvy4XBHLrD///FPS0tKS/P395Y4ii/z8fGnt2rVSdna28rFS2g4cOCCpqalJS5YskS1DebF582Zp9uzZyp/x58+fS5cuXZIkSZIuXbok1a5dWxo9erTk5+cnBQYGSi1btpS6d+8uXb58WcrIyJBq1KghpaamynkKFUr79u2lJUuWyB3jLwoKCqTffvtNcnNzk3R1daUqVapIQ4YMkYKCgqScnBy54/3PEhMTJUC6c+dOiR8rPj5eWrhwodS4cWNJoVBIlpaWkre3t5SRkVHixy5OXl5ekp6enpSfny93lErl/Pnzkrm5udSjRw/p7NmzkiRJ0sOHD6Xk5OQ3bmdmZiZFRET85es3btwoNW3aVBo9erSUnZ1dKpnLktevX0tOTk6Sjo6OFB0dXWrHHTdunPTzzz9LsbGxUnh4uOTt7V1qxy7rbt26JQHSlStX5I5SLvz444+SsbGx8u/p6elSSEiINGPGDKlJkybSlClTxPOyIAjS/6/lvrXeW/nWpwiC8N6aNGmCtrY2165dY+fOnXTo0IHZs2ezZs0afv/9dxYvXkynTp1o1aqV3FHLnKioKHJzc+nTp4/cUWShoqKCu7s7ampqb+0AefHiRYkcf8+ePQwbNoyVK1eydOlS0YXyX6T/WjZ/6NAhIiMjOX78OAC1atVSdja9evUKX19funbtysGDB7lz5w67d+8mOjqa9u3bU6NGDY4fP14inZaVlaOjI/7+/nLHULp79y7Lli2jRYsW9OzZk/j4eL777jtlJ6yNjU25XK6an58PUCojZVq3bs2KFSu4e/cukZGRNGvWDHd3d/T09Bg2bBiBgYHk5uaWeI4PFRgYyIABAyrlknc5denShZMnTzJ06FAmTpxIeHg4V65cYenSpbx8+RIoHOPSqFEj7t27p/y67OxsAKZMmcLEiRPx8/Pj2rVrspyDnDQ1Ndm/fz8DBw7E0tKS8PDwEj1e0XNL9erV2bBhA6tWrWLjxo3MmzcPS0tL5b+LVIlX5n788ccYGRkREBAgd5RyoXbt2mRnZxMZGUl2djY1a9bEysqKtf+PvTsPqDH9/z/+PCEtlowsKWv2NVlbJEsKUciWShjbyDLCIMbS2NexNoy1IstQlFJK2ZfsW6lsoSKyt6hzfn/07fzGfMwMo7pbrsc/05zOuc/rdk7n3Pf7vq73tWIFvr6+3L9//19blQiCIIDoKSwIwleysLCgc+fOHDhwgJ07d5KRkaFcRCo0NJSxY8cW2MU7pBQYGIipqWmxXoxPJpN9tnCgUCg4ceIEnTt3xs7ODjMzM+UJUm7YuHEjw4YN49dff2XatGm5tt2iIj09nTdv3gDZr9GzZ89QU1PDwsKC06dPk5iYiEwmU57UmpiYYGVlxQ8//ECpUqV49+4dRkZGKBQKZRFLTCPPXX379uXGjRvExsZKliElJYVNmzZhamqKvr4+u3btYtiwYTx48IDw8HCGDx9O+fLlJcuXGzIzM4H8KQrnUFFRwdTUFA8PDxISEti1axcfP36kX79+VKtWDRcXF86dO1cgi0WpqamEhobSs2dPqaMUWxMmTODChQuYmZmhqalJbGwsZcqUITU1lWPHjnH37l2aNWumvH/p0qW5c+cO5ubmvHjxggsXLtC4cWMOHDiAq6sr4eHh0u1MPitRogSbN2/mhx9+oGfPnvzxxx+5uv3MzExevHhBamoqJUqUALIv8MXExNCkSRN+++03EhISePPmDZMnTwYo9hesbWxsRFH4C/Xs2ZOuXbuyadMmjh49SnJyMu/evePDhw/o6emRnp5eYNbyEAShYBNFYUEQvsrw4cPZsGEDkD1S5ddff2X79u0EBwdz+vRpmjVrpjyxFv6/oKAgrKyspI5RIG3fvp1t27ZRv359duzYQcOGDVm7dm2ubHvFihW4uLiwZcsWxo0blyvbLEo8PDxo1KgRrq6ubN26ldTUVCpXrsyYMWMwMzPj+fPnHD16FMg+gf7w4QOxsbHK0SflypWjUqVKQPbJbGEcHVoYNG3aFH19/XwfLZyRkcGhQ4fo378/VatWxc3NDUNDQy5cuMCdO3eYOXMmNWvWzNdMeSnnuyungJPfSpcuja2tLfv37ycxMZFFixZx8+ZNjIyMqFevHnPnziUmJkaSbJ8THh5OZmYmXbt2lTpKsVaqVClKly5Nw4YN+fjxI02aNKFPnz6sWbOGvn370qZNG+V9ly9fzi+//EKXLl1YvHgxqqqqjBo1ip9//hk1NTWGDh3KuXPnJNyb/CWTyVi6dCnu7u4MGDCALVu25Mp2k5OTadSoEVOnTsXCwoKwsDCePXtGtWrVSE9Pp3LlylStWhWA1atXc/v2beUCYcWZra0tFy9e5MmTJ1JHKRQmTJhAvXr1WLp0KQMGDGDp0qUsXbpUOWuzV69eUkcUBKEQEAvNCYLw1Xr27Mm+ffvQ0NDg4cOHVKhQgdTUVObMmYOamhqvX79GX1+fWbNmSR21QHj48CG1atXi2rVrNG/eXOo4BcrBgwcJDQ3F2NiYwYMHI5PJWL9+PQkJCfzyyy//ebsKhQJ3d3fc3d3x8vJi4MCBuZi6aEhLS6Nr1664u7ujq6vLhg0bUFFRYeXKlcr7LF++nPj4eMaMGUOjRo14/fo1O3fuZP369WhoaKBQKPDz86NGjRoS7knxMG3aNE6dOsWZM2fy9HkUCgUXL17E09OT3bt38+7dO3r37o2joyNWVlZFuvB/48YNmjdvTnJyMhUrVpQ6jtLDhw/ZvXs3np6e3L59m3bt2uHg4MDAgQOVF2WkMG7cOGJjY5UXjoSCwd/fn3fv3tGuXTt0dXVRVVUlNTWVhQsXcvbsWRYsWEC7du0A6N+/P7Vr12bw4MG0bNmSHTt2UKpUKezt7SXei/y3adMmxo4dy+LFi5k6deo3bWvt2rVcuXKFrVu3snv3bqKjo9HS0mLMmDHMmTOHwMBAzp49i6amJlu3bmXHjh0EBQWhrq6eS3tTOMnlcvT09Jg9ezZjx46VOk6B9eTJE3R1dZX///HjR/z8/Dh+/DhGRkbUqlULQ0NDNDQ0JEwpCEJB8CULzYmRwoIgfLWAgABSUlLo0aMHNjY2uLu7069fP7S0tJgwYQIuLi5s3LixQE53lUJQUBDVqlX7ZAqnAM+ePePEiRO0bduWfv36IZPJuHXrFrdu3aJv377/ebsKhYLp06ezYMEC/vjjD1EQ/pPXr18rf46NjSUzMxN9fX3q16/PkiVLCAoK4vr168r7WFpaolAoCA4OBqB8+fKMGTOGPXv2sGzZMq5cuSIKwvmkT58+nD17loSEhDzZ/sOHD1mwYAENGzakXbt2XL16lUWLFpGYmMjevXvp1atXkS4IgzTtI75EzZo1mT59Ojdv3uTKlSuYmpqycOFCqlWrhrW1NT4+Pvk+ylChUBAQECBaRxRA1tbWDBo0iNq1a6OqqkpsbCyrVq3i/PnzbNy4UVkQ3rVrFykpKYwdO1bZM97Pz4/U1FQgu3f4kSNHOH78uGT7kp9GjRrF7t27cXNzY8aMGd90DPv06VMePXoEwODBg+natStv3rxh3bp1DB06FGNjYwwNDZk1axZubm7MnDmz2BeEIbudTu/evUULiX8xdOhQ5bHamTNnMDIyYsuWLVSqVIkaNWpgamoqCsKCIHwxURQWBOGryeVytm/fjrq6OgcOHMDJyYkPHz4wevRo6tSpQ6tWrdDT0ytWUxD/SWBgIFZWVsW+V9xf+fv7Exsbi62tLaVLlyYhIYHjx49Tt25d9PT0/tM25XI548ePZ+3atfj7+9O7d+9cTl34KBQKsrKyWLhwIQ4ODowcOZJDhw7RoEEDHjx4wPPnz4HsqeujR4/G1dVV+dhmzZrRsmVLoqOjCQsLA7KnKrdo0YIuXbpIsj/FVbt27dDR0cHX1zfXtvn69Wu2bNlCx44dqVWrFjt27MDBwYF79+5x8uRJRo4ciZaWVq49X0EndfuIfyOTyTAwMFCO4A8MDERbW5uRI0dSpUoVnJ2dOXbsmLL/d166desWDx8+FEXhQsDf35+4uDh+/vln6tWrB2S/1/38/HB2dla2gAkNDaV06dIMHjyYkJAQTE1N2bp1K/369WPVqlVS7kK+GTBgAIcPH2bNmjWMHTv2q/6Wjhw5wuLFiwEYMmQImpqaHDlyBABTU1NlS6b4+Hg8PDyYM2cO7du359ixY1haWubJ/hRGNjY2hIWFKdc6ED4ll8uxsrLCwcGBw4cPs3TpUnr37o2LiwtyuZzevXsr33eCIAhfQhSFBUH4aioqKhw6dAgjIyPq1KlDs2bNaNOmDT/++CPbtm3D2toaLS0tmjRpInVUyWVkZBAaGir6CX/GlStXaNu2LeXKleP58+cEBASQkJBAmzZtqFy58ldvLysrixEjRrBz506OHj2KhYVFHqQufGQyGffu3SMqKop58+ZRtWpVdu3axfv37xk+fDhTpkxR3nfixIkkJiZ+Unjs0qULKioqvHr1Sor4wv9RUVHB1tb2m/sKf/z4kYCAAAYOHEjVqlWZNm0aTZs25ezZs0RHRzN79mxq166dS6kLl5wCUEEbKfw5JUqUoGvXrmzfvp2kpCQ2b95McnIyVlZWVK9enSlTpnD16tU8m7Hj7+9Po0aN0NfXz5PtC7ln0qRJLF++HFNTUyD7QuHLly/R1tamcePGqKiooFAo2Lt3LwMHDiQwMJDdu3czevRo9u/fz507d7h9+3axWS/C0tKSkJAQ9uzZg729PRkZGf/6mP379+Ps7KycOVOlShWaNGnCiRMnlIt9mZubo6KiolyXw97eHmtra3Gs/BedO3emdOnSBAUFSR2lQFJRUWHKlCk4ODjg5uZGqVKlcHJyomfPnsyfP581a9awfft2qWMKglCIiKKwIAj/Sbt27QgNDSUxMZHY2FhkMhlWVlbs3LmTunXr4uHhQbly5aSOKbnTp0+TmpoqCpSfUadOHa5evcr27dsZM2YMcXFxmJiY0KFDh6/ellwuZ9iwYfj5+REaGvqftlHU5EwBBvDy8qJixYoYGhoydOhQXrx4gVwuZ/78+dy/f/+TqZr9+/f/pG9tjRo1mD9//je19BByR58+fTh+/DgpKSlf9TiFQsGlS5eYOHEiurq69O3bl8zMTHbv3k1CQgLr16+nffv2xX42Q0FtH/FvNDQ0GDRoEP7+/jx9+pSZM2dy6tQpWrZsSbNmzVi8eLFyKntuEa0jCpcKFSoof5bJZMhkMnx9fZV/82vXrqVMmTJUrFiRkydP0rhxY8aPHw9ASEgIaWlphe7v4lsYGxsTERFBREQEvXv3/uT79K/27t3LoEGDCA0Nxd7enrS0NCpVqoSLiwtpaWkcPHhQOdOma9eulChR4osKzcVV6dKlsbKyEi0k/oWzszPNmzcnJCSES5cuKW+/ffs2mpqaEiYTBKGwKT7f7oIg5CpXV1dGjRpFw4YN6d69OzVr1mTMmDGMGTNGeZ+kpCRKlCiBtra2hEmlFRQURPv27YvVFOwvNXr0aKKjozl+/DjNmzf/ZBqrQqH42wKVXC5XntTmyMrKYtq0afz000/FftRNYmIiP//8MzKZjBo1auDm5oadnR3Xrl0D4O3bt9y4cYOwsDB69erFhg0bmDlzJhUqVKBx48bcunVLWezJeR2+++47KXdJ+D/m5uaUKVMGf39/HB0d//X+8fHxeHt7s3PnTu7cuYOxsTHz589nwIAB4jX9jILePuJLVK5cGRcXF1xcXIiJicHb25vff/+dGTNm0LFjRxwcHLCzs/um76QXL15w5syZb1oMVJCWlpYWPXr0wNfXlz/++INt27bh5+dHfHw8pUqVokOHDnz33Xe8evWKxMRE5Wynly9fcvXqVQIDA1EoFCxfvlziPck7zZs35/Tp08ybN085mvpzxyU6OjrUr1+fZs2a8fTpU77//ntKlCjBwIED6dOnDzdv3sTNzY3q1avz5s0b9PT0inx/9m9la2uLi4sLHz9+FP9Wf6Ny5cqsWbOG9PR0XFxcCAsLo2zZspw/f57Vq1dLHU8QhEJE9iXTymQymUIsGCUIwl89efKEpKQkHj9+jImJCampqRw4cICYmBiuXLlCTEwMvXr14vfff5c6qmSaN2/OwIEDcXNzkzpKgfbs2TNly4i/nnjJ5XJUVFSU/4XsIvBfCzf/VEguTtavX8+zZ89o27Ytc+fOZfTo0YwYMUL5b7N06VLu3LnDmzdv+PjxI3v37sXb2xsfHx9UVVVJSEjAw8ODtm3bSrwnwuc4OTnx7t07Dhw48Nnfv337lj/++IOdO3cSHh5O7dq1cXR0xMHBgbp16+Zz2sIlNDSUbt265UtP3vykUCg4f/48Xl5e+Pj48O7dO6ytrXFwcKB79+6ULl36q7bn7e2Ni4sLz549EwWbQuzkyZOsXr0aQ0NDGjRogJ2dHb/99htRUVHKHsKhoaGEh4djYWFBq1atWLlyJYmJiWzcuJGAgAC6d+8u8V7kPYVCgUKhUB5/5EhISKBSpUqULFmStWvXcu3aNY4cOcKYMWOoU6cOv/76K/369WPq1KlERUURGRlJ6dKlGTRokER7UnikpKRQqVIljh49KtYv+Bt/PuY9duwYBw4cQFdXF2dnZ3R1dSVOJwhCQSGTyVAoFP94giyKwoIg/Cc5ByNRUVFs2bKF6dOnk5mZyYIFC9i5cyenT59GV1eXxo0b8+jRo2I17TDHkydP0NPTIzIyklatWkkdp0BbtGgRZcuWxcXF5W/v8+DBAx49esS5c+fYtm0b06ZNY9iwYfmYsuBKS0tDTU2NtLQ02rVrx8qVK+nSpQvLli3j8uXL7N69W3nfnJE3cXFxzJ49Gzs7O/r27cubN2+4ceMGdevWpUqVKhLujfBPDh48yJAhQ0hOTlauLp6ZmUlISAienp74+vpSunRpBg4ciKOjI8bGxuJiyRc6evQovXv3Jj09Xeooeebjx48cPXoULy8v/Pz8UFdXZ8CAATg4OGBsbPw/ha/Psbe3Ry6X4+Pjkw+JhfzUuXNnOnXqxOzZs4mJiWHfvn2oqqoyceJE5QWAkSNH8ujRI44ePao8FvzchdqiLCAggJUrV7Jx40bq16/P6dOnmT9/PgYGBixZsgSACxcu0LVrVwIDAzExMZE4ceHTpUsXGjduzNq1a6WOUmD93WAIMUhCEIQcX1IUFj2FBUH4T2QyGRkZGaxZs4aSJUuioaFBpUqVWLNmDRUrVkRdXR0tLS10dHTw9vaWOq4kgoKCqFy5Mi1btpQ6SoE3Y8YM+vfv/8ltHz58ICYmhl9++QUnJycmTpyIu7s7a9asoV69enTq1AmgyI3q+xq+vr40bdqUYcOG4evri5qaGlOnTlUuFPbu3TsUCgWxsbHI5XIAnj9/DoC+vj5v376lVq1aAJQrVw4TExNREC7gclapDwoK4sqVK0yePBk9PT1sbGz48OEDnp6eJCYm4uHhgYmJiTgx/AqZmZlFvrBVqlQprK2t8fHxISkpiZUrVxIbG4uZmRn6+vrMmjWLqKiov318ZmYmgYGBWFtb52NqIb84OTmRlJREREQELi4upKen0717d2VBODY2lq1bt7J582YA5edLSkoKJ06cwN3dnQ8fPkiWP7+0a9eOCxcusGHDBu7evYuJiQkDBgyga9euQPYiw23btqVDhw68fPlS4rSFk42NDX5+fnm2WGZRkPP3J5fLlcd4f75dEAThS4iisCAI/5mqqioRERGYm5ujrq6uHGFUt25dTpw4AcCUKVN48+aNlDElExQUhKWl5ReNvBL4n2JkTEwMFhYWXLp0iZ9//hkvLy9MTEwYPXo0CxYsoFatWpw4cQIrKyuSkpIkSi2dsLAwJk+ejIuLCw0aNGDs2LE8fvyYPn36UKdOHaKjozl79ixqamoMGjQILy8vUlJScHBwYM6cOfTv3185RVMoPFJSUqhTpw4jRozA0NCQM2fOMHv2bJ4+fYqvry/9+vX76nYAQrbMzMxiNaulXLlyODs7c+zYMeLj4xk3bhz+/v40atSI1q1bs3r1ahITEz95zJkzZ3jz5o2yx6xQtJiYmHDnzh02bNiAnp4e06ZN+6RPv729PRMmTKBGjRpkZWUhl8vx9fVlwYIFdO7cmTt37vD+/XsJ9yDv5YzC1NHRITY2Fh8fHx49esSIESOwsLDg0aNHlChRgrt37xIbG/vJIn/Cl7OxsSE+Pp6rV69KHaXA+HOB/M8/q6ioiHMNQRD+s+Jz5CsIQp6wsbFh48aNvHz5EnV1dQIDA3n//j2dO3cGYNCgQUX+BOFzcqZzb9y4UeoohVaLFi2YMmUKq1atIi0tjeDgYJ4+fcqIESNo1KgRAGZmZhgbG2NkZIS/vz+NGzeWOHX+iYuLw8zMTLm449GjR9m3bx8//vgjAHXq1CE4OBiAZcuWcfbsWZycnPjpp5+4c+cOderUYc+ePeJEohDI6SHs6elJaGgoFStWJC0tjZs3bxb7hRVzU1ZWVrEqCv+Zrq4uU6ZMYcqUKdy4cQNvb29WrFiBq6srFhYWODg4YGtri7+/P+3bty/WC8gWZfXq1SM0NJQPHz6grq6unBWmqqqKj48PcXFxXLhwAcguRMlkMmxtbXn27BmWlpb8+OOPn1xojI6OpkGDBlLtTp6QyWRUrFgRW1tbOnXqREhICH5+frRp04ZHjx5x6NAh3rx5w+XLlxkxYgSmpqZSRy6UatasSYsWLfDz8xMz7v7Pn0cA5/xcXFu4CIKQe8SZoCAI32T69Ol07NgRNzc3du/ejaamJps3b6ZGjRpA9sFKmTJlJE6Z/86dO8ebN2+wsLCQOkqhlDMCwsXFhaVLl9KxY0c8PDyYMmUKLVu2pGTJknz8+BGAefPmsXDhQjp37qw8WS2K1q5dy+zZs7lz5w5yuRwdHZ1PeiqbmpqSmZn5yWNyRum3a9eOuLg45HJYU6h3AAAgAElEQVQ5lpaWTJo0iSVLloiCcAGWlZVFcHAwjo6OVKlShQkTJlCrVi0iIiKIiooiMzOTp0+fSh2zSCkO7SO+RLNmzVi8eDEPHz4kNDQUPT09xo0bR5UqVdi0aRP169f/n88aoWjR0NDI6UOIqqoqABMmTGD9+vUA//P6BwYG0rVrV+rXr6/8/dmzZ2nUqBFGRkZcv349f3cgFyUnJzNhwgQ2bNjAsWPHlLc/fvyYe/fusXLlSsLDw+nQoQNpaWns3LmT1atXc/jwYebNmydh8sIvp4WEAPfu3cPPz4+QkBCOHDnCjRs3SE9PVxaH9+3bx7NnzyROKQhCYSTOBgVB+CblypXjxx9/5M6dO7i7uzNr1izlKM6QkBDWr1/PlStXJE6Z/wIDA2nbtq0YTfUf5ZyMApQvX542bdoQGRnJ48ePlSeoOT0Onz17plwBfdasWSQnJ0sTOg/NmTOHbdu2UblyZdavX8+CBQuwtramdevWyn+n6Oho5SjH1NRUTp8+Tf/+/dmzZw9Tp07F3Nxcwj0QvtT169eZOnUqNWrUoGfPnrx69Ypt27aRkJDA5s2b6dChAxUrVqRTp04cOHBA6rhFSnFrH/FvVFRUMDc35/fffycpKYmlS5fy+vVrvLy80NXVZdKkSURGRoqen0VYTsFpxYoVlClThkGDBgFQsmRJ5eu+ceNGVFRUsLa2pnz58kB2+6y9e/fi6enJ8OHDMTMzY/LkydLsxDd48uQJdevW5f79+4SGhrJ69WrCw8MB6NOnD2XKlOHevXucPn0aExMTsrKyePnyJXXq1BGjW3OBra0tV69e5eHDh1JHkdTWrVuZN28eXl5enD17lsjISHbv3s2qVau4dOkSAMeOHaNixYoSJxUEoTASRWFBEHKFqqoq9evX57vvvlPeZmFhQcWKFWnVqhWdOnVSjuwsDoKCgkTPxW8kk8l4//49hw4d4qeffuLkyZNoaWkpf5+SkoKPjw9Dhw5l7NixTJ48md27d6Otrc2tW7ckTJ77Hj9+zLhx4xg/fjwrV67k4MGDnDx5Ek1NTT5+/KgckZ8zQl9dXZ2GDRtiZWVFQEAAgwYNYubMmWJkcAGVkJDAihUrMDAwoEWLFoSHhzN9+nSePn3K4cOHGTBgAOrq6p88pk+fPvj5+X2yuIzwbYpz+4h/o6amhlwup3r16iQkJDBv3jwiIyNp06YNjRo14pdffuH+/ftSxxTyiKurq7K3a2ZmJnK5HBUVFZKSkti7dy+DBg1SLloaERHBpk2biI6OZsiQIYwcOZJ79+5hY2MDUKguIly9ehVra2sOHz7M8uXLqVGjBvfu3QOgTJkyjBo1ijZt2rB9+3Z8fHw4ffo0GRkZEqcuOgwMDKhRo0axHy28bt06bG1t2bdvH1OnTsXe3p4uXbqgUChwdXUlKCiI33//Xcx0EQThPxFnh4Ig5Io/97nKyMhg8+bNtGnTBkdHR7p168bgwYOLzcFKYmIily9fVo5eFf47TU1Nli5dioGBAU2bNsXQ0BDIHrU0e/Zstm/fzvjx49m4cSPTpk2jYsWK9OjRg44dO+Lu7i5x+v8mPT2dVatWceTIEeRyOQqFgpiYGOXIaFVVVSZNmsTMmTOV/y+Tybh+/ToGBgZAdquJFy9e8OOPP7Jz505ln2Gh4Hj//j3e3t5YWVmhp6fHr7/+Ss+ePblz5w4XL15k/Pjx/7gIoK2tLYmJiZw/fz4fUxdtYqTwPwsICMDa2hptbW3GjBnDqVOniIuLw8HBAU9PT+rUqYOpqSkeHh68ePFC6rhCLitbtiyQPUo45wLjsmXLaNq0KR06dFB+R6Wnp2NiYkJqair16tXj0qVLfPfdd3Ts2BEoXEXh+Ph45ZT82rVro6+vz8aNG1EoFBgZGeHs7MyhQ4ewsrKiatWqrF69mmrVqkmcuuiQyWT07t272BeF+/Tpw759+3jy5Anq6urUrVuXLl26MGPGDCwtLfH09OTdu3dSxxQEoZASRWFBEL6ZQqEgKysLDw8PjI2NUVdXZ+3atQwZMoRXr14RFBTEqFGjis0oxeDgYCpWrEjr1q2ljlIkqKmpKVfvDg4OZvLkyWzevJmBAweyefNmevTogaqqKmfPnqVv3740atSI27dvs2fPHkaPHi1x+q/z+vVrjI2NuXDhAl5eXsyZM4f4+HicnZ1ZtGiR8n5OTk5kZWWxadMmAO7evYuKigoBAQHUq1cPX19fdHR0pNoN4W9kZWURGhqKs7MzVatWZezYsejq6hIaGsqDBw9YsGABDRs2/KJt6ejo0L59e9FCIhdVrVqVDh06SB2jQHr37h3Hjx/H2tr6k9vr1KnDrFmziIqK4uLFi7Ru3Zo5c+ago6ODra0t+/fvJy0tTaLUQm7688V/yG51ExISQv/+/alcuTIAt27dolu3bkybNo3jx4/Tt29fjh8/DqCc1aCiosKLFy/w9vbm119/LXBF4ujoaC5fvszbt28ZNWoUK1asID09HYAuXbooZyxpaWmxZs0aTExMlKODi+MaGnnNxsaGiIgIUlJSpI4iGXt7e5KSknBzc2PHjh1cunRJebGiVatWXL58+X/+PgVBEL5U8ajQCIKQp2QyGatWrWL27NkMGDCAly9fcv36dSZNmkTZsmWVox2Li8DAQLp161ZsRkbnp7t375KUlKRc1KV69eoAPHz4kB07dnD06FG6d+9O5cqVOXHiBCkpKdy+fVvi1F9GoVAQGxuLlpYWu3fvZt26dRgYGPDzzz8zePBgNDU12bFjh/L+c+fOxcvLS/nYmzdvsn37djw8PAgNDf2klYsgrVu3bjF9+nRq1aqFpaUlz58/Z/PmzSQmJrJlyxbMzc3/00Wzvn37cvDgwWL1+ZqXLC0t2b59u9QxCqRjx45RokQJOnXq9Nnfy2QyWrduzerVq3ny5AmHDh2iTJkyDB06lKpVq/L9998THh4u2p0UIc2bN8fb2xtDQ0NUVFS4du0aU6ZMISsrS1kkffbsGZGRkUB2MTg1NZWFCxcyZMgQfvjhB968eVOgilkeHh50796diRMnYmdnx86dO6lXrx6lS5cGoHr16jx8+FBZkPPw8CA+Pl651oGQ+zp27EiZMmU4cuSI1FEko6+vz759+zAwMODcuXN4enoyd+5cDA0NWblyJS4uLmhqakodUxCEQkr2JScSMplMIU44BEH4JwqF4pMDe7lcjkwmK1AH+/khKyuLypUrs2rVKpycnKSOUyj99b30V2/fvlVOYwW4ffs2jRs35saNG9y9e5fRo0fj6empbN+RlZVVYAv0ycnJxMbG0r59ewCuXbuGubk5SUlJqKqq8urVK+bPn4++vj6tWrWiT58+REdHU7ZsWfbs2YOfnx/e3t7ExMQQFxdHjx49JN4jIUdSUhK7d+/G09OTy5cvY2hoiKOjI4MHD6ZKlSq58hxxcXHUrVuX69ev06xZs1zZpiB8zsiRI0lMTOTw4cNf9bh3797h6+uLl5cXISEhVKtWjSFDhuDg4EDTpk3zKK0ghSdPnjB+/Hi2bdtG+fLliY6OxtHRkYULF2JsbMy0adNISEhAQ0ODWrVqcf36dfbv30+pUqXIyMhAVVWVtLQ01NTUJMkfFRWFk5MT+/fvp0aNGvzyyy9ERkayefNmKlWqREZGBsnJyQwYMICjR48yefJkrl27Rnh4+N9m/rfjGeHL2Nvbk5mZyd69e6WOIrm0tDTi4uJ48eIFpUqVoly5cjRp0kTqWIIgFFD/t3j7P34RiZHCgiDkCplMhlwu/2R6YHE8EL548SIvX77E0tJS6iiFUlhYGKmpqf84mqxs2bLKkZEHDx5k3759ADRp0oR+/foxYMAArl27prx/zgjMrKysPEz+33h7ezNz5kzl/tatW5eOHTuybt06IHt66vDhwzl8+DDNmzdn+PDhODs78+OPPxIQEEDNmjVRUVGhQYMGoiBcAKSmpuLj40PPnj3R1dVl+fLlWFhYcPPmTS5dusSkSZNyrSAM2aOHmjVrJlpICHlKLpcr+wl/rTJlyuDg4EBQUBBPnjzB1dWVY8eO0axZMwwMDFi+fDlPnjzJg9RCftPW1iYrK4vWrVvz008/YW5uTteuXblx4wZ2dnZUqlSJ7du3s3LlSh49eoS9vb2yD3GJEiV48OABw4cPx8PDQ5Lva01NTTQ0NMjMzARgxowZnD59mv379wPZ/furVKmCTCajcePGxMTEcO7cub8tCGdkZHDz5s1i3fYgt9jY2BAYGKhs41Gcqamp0aRJE8zMzDAyMhIFYUEQvpkoCguCkGtUVFSKTd/gvxMUFIShoWGuFn6Ki61bt2JhYcGBAwf+9X2Uc8GhRIkSxMTEANnvv7i4OKKiopSroP/5vu/evSMyMrLALIAkl8s5fPgwCQkJyiJwyZIlGTx4MGfOnCE+Ph7Inq7arFkzjh07xty5c5k8eTIVK1ZEW1tbLCBXAMjlcsLDwxkxYgRVqlTh+++/p1KlShw9epSHDx+yePHiPD1py2khIQh55cqVKyQkJHzzhaeqVasyadIkIiMjuX37NtbW1qxbt47q1avTtWtXtm/fzps3b3IptZDfSpcujZ+fH3PnzkVLS4vVq1ezcOFCatWqxY0bN7h9+zZly5bF39+fjx8/MnDgQCC7/ZO9vT2urq5oaGjQtm3bfJ/dk5GRQaVKlfjuu++Ijo4mOTmZixcvUr58eS5cuABkf9ZnZWXx9OlTmjdvTlhY2D9uUy6X4+zsjLm5OYmJifmxG0VW9+7dSU9PV/anLs5yLloIgiDkFtE+QhAEIRe1a9cOCwsLfvnlF6mjFCrr1q1j4sSJbNiw4asWhzt27BgDBgxg9+7dZGRkcPjwYVRVVZkwYQIVK1bk9evXbNu2DXV1dX7//XcePHjAH3/8QZ8+ffJwbz7vxIkTlChRAhMTExQKBYmJifz4448MHjyY1atX4+3tTbVq1bh//z4bN27k9evX/Pbbb6Snp9O1a1dcXFyUJ9GC9O7cuYOnpyfe3t48fvwYCwsLHB0dsbW1zdfefteuXcPAwIC4uDjq1KmTb88rFB/z58/nwIEDXL16Nde3LZfLOXPmDF5eXuzdu5fU1FRsbGxwcHDA0tJSOZJUKNw+fPjAuHHjCAoKomzZshw9epTatWuzdOlStm3bxsOHD/ntt99wdHRUPiYzM5OSJUvmaa5BgwahpaXFq1evWLx4MYGBgfj6+hIfH0+rVq2Ij48nOjqa2NhY5ef606dPqVat2hdt/+XLl/To0YPk5GRCQkKoXbt2Xu5OkWZlZUWtWrXw8PCQOoqkdu3ahYWFBZUqVZI6iiAIhcCXtI8QRWFBEHJdfhzIF0TJycnKBc5MTU2ljlNoLFmyBDc3N7Zt2/bJCeGX2rVrF5s3b+bFixdYWFigra2NQqHg/PnzVKhQgUqVKqGvr8+0adNYsWIFI0eOzIO9+HdjxoyhQoUKLFq0SHnbhQsXqFevHjNnzkRDQ4MVK1agUChITk6mY8eOdOjQgbZt2+Lj48P8+fMxMjKSJLuQ7fnz5/j4+LBz504iIyNp0aIFjo6O2Nvbo6OjI0kmhUKBvr4+48aNw9XVVZIMRYXo//l5bdu2pVu3bnl+sTM9PZ3AwEC8vLw4fPgwZcuWZdCgQTg4ONCuXTvx2hRSf+7r7+TkxJEjRxgxYgQmJib079+fIUOGoK+vz+XLl3F1dcXY2PiTx/v6+hIVFaVcTCu33gebNm3C29uboKAgdu3axb59+3B3d+ft27ckJSVRs2ZN6tevz8SJExkxYgSdO3dGLpd/9Yy4d+/e0adPH27dukVISIiY7v8fbdy4EXd3dx4/flxsZyW+fv2aSpUqcfjwYdGmThCELyJ6CguCIIlXr17RvHlz7ty5I3WUfBUcHEy5cuWUi4YJ/0yhUDB79mxmz57Nnj17/lNBGLIXIPHz8+PEiROsWLECGxsbtm/fTrly5di+fTtLlizBx8eHqVOnKgvCO3fuZO3atQQEBOTmLv2jR48e0bNnTwBlX7y2bduipaXF4MGDuXz5MhcvXkQmk1GpUiV27dpFkyZNOH78OB06dBDvK4mkpaWxb98+evXqRbVq1Vi0aBHm5uZcu3aNq1ev4urqKllBGLIP9kQLiW+Tnp5OREQEK1eu5PDhw7x8+VLqSAVGYmIiFy9eVH525aXSpUtja2vL/v37SUpKYvHixdy8eRMjIyPq1avH3Llzle2ChMKjRIkSKBQKTpw4QVRUFMnJySxZsgQvLy/mzp3L1q1bcXFxQUNDA19fX+Xj9u/fj4uLC25ubqSmplK6dOlcvTCQlpZGmTJlUFdXZ8SIEQwfPpz169eTkZHB4MGDMTY2Rltbmw8fPnDmzBmA/1SMLFOmDP7+/hgZGWFmZsb58+dzbR+Kk969e5OQkEBkZKTUUSQTGBiImpoa5ubmUkcRBKEIEUVhQRByXc5iI3/88YfUUfJVUFAQFhYWxXKU9NdSKBS4urqybNkyfH196dev3zdtr1y5cmhpaaFQKGjcuDHe3t6cOnWKZcuWYW1tTdOmTZk9ezYAdnZ2nDx5EhUVFb7//nt27tyZG7v0ieTkZOXPOf3fqlWrxvLly/nhhx/44Ycf8Pb2Jjk5GZlMRrNmzejUqRNr165VPs7AwIAJEyawdetWfv75ZzFKLh/J5XJOnjzJyJEjqVq1Ks7OzmhpaREQEEB8fDzLli2jefPmUsdU6tOnD2fOnBF9K79QRkYGr1+/BrJf65UrV9K9e3du3LjB4sWLMTMz48qVKxKnLBgCAwPR1tambdu2+fq8WlpafP/994SHh/Pw4UO+//579u3bR/369Wnfvj3r1q3j+fPn+ZpJ+O9kMhlmZmb89ttvAERHRxMZGYmLiwsA5cuX59WrV8hkMp4/f86UKVMYPHgwKSkpREREMG/ePEqVKsW3zlxNT0/n2bNnADRo0AB1dXWioqIAGDBgAJaWlnh4eHD37l3lY5ydnalbt+43PW/p0qXZs2cPffr0oUuXLhw7duybtlcc6erq0rp1a/z8/KSOIhk/Pz+6d+9O6dKlpY4iCEIRIorCgiDkCTs7O+WKzcWBXC4nKCiI7t27Sx2lwJPL5YwdO5ZNmzZx5MiRb1686M9kMhkZGRm0adOGixcvKkea5SzkduXKFT58+MDmzZsZN24cq1atIiQkBOCbTzZzrFmzhpkzZyqLTiVLlkShUBAdHc3Tp08xMDAAstteLFmyBIAKFSrQt29f4uPj8fb2/iSPqqpqruQS/t3du3eZPXs2+vr6dOzYkQcPHrBmzRqSkpLw9PSkW7du+b4A0pcwMjKiSpUqxfpk+WucP3+eAQMG8OLFCy5fvsy5c+fw9/dn+/btnD59mhEjRuDm5iZ1zALB39+fHj16SPq+r1GjBtOnT+fmzZtcuXIFU1NTFi1ahI6ODtbW1vj4+PDhwwfJ8glfrmXLlgDUr18fMzMzAgMDgewWEZGRkQwdOpQePXpw9OhR5cXiP8+CkMlkPHr0CE9PTzw9Pb/6dXdxceGXX34hLS0NIyMj0tPT8fb2Vm5n8ODB1K1bl3Hjxikf07t3bwYNGvRN+w3ZxwKbN29m7Nix9OzZkwMHDnzzNosbGxubYvs9l5GRwZEjR7C1tZU6iiAIRYwoCguCkCfs7Oy4du1asZnqeeXKFZ4/fy56fP2LzMxMnJ2d2b17N8HBwXTu3DnXnyOniOrq6krLli2pXbs2MTExzJ07l8DAQO7fvw/Ax48fef36NWlpaQDf3O4kKysLgBs3bnDw4EEuXLigvE0mk1GtWjVKliyJk5MTW7ZsYcyYMTx79ox79+4BoK+vz9ChQ6lYsaLyMULee/HiBevXr6d9+/Y0aNAAX19fxo4dy6NHjwgJCcHJyYkyZcpIHfMfqaioYGNjI1pIfCFDQ0MMDQ0xMzNj3bp1qKuro62trfx99erVxQrvZI+qDA4OzpfWEV9CJpNhYGDA8uXLefToEUePHkVbW5uRI0dSpUoVnJ2dOXbsmPJzVyi4ZDIZQ4YMYdasWRgaGuLu7s66des4fvw4AGFhYXh4eNCjRw+8vLyUF1mDgoL4/vvvGTNmDGfPnuX9+/df9bytWrVi27Zt7Nq1C01NTdasWUNYWBgbN24kPj4eyJ55Ua5cOeVnQG5+F8tkMpYuXcq8efMYMGAAW7duzbVtFwe2trbcunWL2NhYqaPku/DwcD58+CAGnwiCkOvEHGdBEPJE06ZNqV+/Pn/88QfTp0+XOk6eCwwMpHnz5ujq6kodpcDKyMjA3t6e8PBwjh8/jqGhYZ4+X5UqVdixYwcA48aNIywsDB8fH65evYqVlRU6OjpcvnyZWbNmAbB8+XIyMzP/czuJnJF0lStXxszMjIMHD9K4cWN0dXWRy+VUqVKFChUqkJaWhpqaGpUrVyYqKgp1dXUA1NXVcXZ2/vYdF/5Veno6/v7+eHp6cuTIEb777jvs7e3x8PCgRYsWhbIg36dPH6ytrXn16hVaWlpSxynQNDU1WbRoEY6OjowZM4bTp0+joqLC3LlzSU5O5tChQ3Tt2lXqmJI7efIkaWlpdOvWTeoo/6NEiRJ06dKFLl26sGHDBg4dOoSXlxdWVlZUrlwZe3t7HBwcCu3fc3FgYWHB3bt3CQ8Pp1mzZlSsWJHhw4czatQoKlWqBGS3XUhKSlJOl+/WrRu3bt2iSZMm9OvXT3k/yP5c/+u0+r8uHqmnp0epUqU4deoUCoWCESNG4OnpyfTp00lISODt27eoqamRmZmZZ4smy2Qypk+fToUKFRg5ciQpKSlikdAv1KRJE+rUqYOfn1+x+zfz8/PD3NxcfL8LgpDrxEhhQRDyhEwmK1YtJIKCgrCyspI6RoGVmppKnz59OH36NOHh4XleEAZYunQpALdu3cLHxwdPT09atGjB3r17qVChAoMGDWLz5s30798fgK1bt/LkyROsra3/83MmJSVhZWWFh4cH0dHRhIWFkZqaioqKCubm5iQlJSn/JqKioihbtiwaGhrfvrPCv1IoFJw5c4YxY8ago6ODo6MjGhoaHDp0iMePH7Ny5UoMDAwKbQGpU6dOaGpq5uviiYVZTv/xEydO4OXlRXR0NG3atGHYsGG0b9+eadOmSR1Rcv7+/nTo0KHAFyE0NDQYNGgQ/v7+JCQkMHPmTE6dOkXLli1p1qwZixcv5tGjR1LHFP6Gubm5coZMmzZtiI6OBuD9+/cEBgZia2vL27dvAbh37x73799HR0cHIyMj5Tbevn37j4vAffz4EchutePg4MCQIUOIiIjg6NGjxMbGMmDAAHr37k3JkiWpVKkSq1atQk1NLa92GYDRo0eza9cupk+fjpubW661sCrKZDJZsWwhoVAoOHToEDY2NlJHEQShCJJ9yReQTCZTiC8qQRC+1tWrV2nZsiX37t2jdu3aUsfJMykpKWhraxMaGipWBP6Md+/e0bt3b2JjYwkNDaVevXr5+vzx8fHY2tqyYMECZeG+RYsW7Nmzh4YNGwLZReucEbu//fYbo0aNAv552uhfRyD91datWzl06BCLFy9WPs+2bds4cuQIL1++5P79+2zZsoVOnTrlyn4KnxcXF4enpydeXl7cu3cPc3NznJyc6Nu3L+XKlZM6Xq5ycHAgNTW12C3y+V/lXLARi/b8L4VCQb169Rg7dmyhHZEXExODt7c3Xl5exMXFYWZmhqOjI3Z2dgW+0F1c3b59G0dHR0qUKIG6ujply5Zl8eLFNG3aFICVK1fy4MEDRowYQYsWLcjIyFAWg/88qlehUHDlyhVGjRpFZGQkkL2eQXJyMl26dOHkyZPExcXh5OREUlISkZGR1KpVK9/3F7IHFfTt2xcnJyfWr19fIPvWFyQRERF07tyZpKSkT9r+FGWXLl2idevWPHr0iOrVq0sdRxCEQkQmk6FQKP5xxIsYKSwIQp5p0aIF+vr6RX608LFjx9DQ0MDY2FjqKAXOq1ev6NatG48ePeLkyZP5XhCG7P6gvXv3pn///vzwww/Uq1ePhg0bKgu1kN26ISUlhfv37/P9998jk8n+p+AbFxfHunXruHTpEvDvfQaHDx+OiooKAQEBylFOw4YNY8eOHaxYsYJ79+6JgnAeefnyJR4eHpiYmFC3bl327t3LiBEjePDgAWFhYTg7Oxe5gjBkt5AICgoiNTVV6igF3oULF5gwYQJz5szh7t27XLhwgb59+9KnTx8OHz4sdTzJ3b17l7i4uG+aOSG1evXqMXfuXGJiYjh79izNmzdnxowZVKlSBTs7O3x9fUlPT5c6pvAnjRs35tKlS/zwww+4ubmxe/duZUE4LCyMmJgYDA0NadGiBZDdRkQmk/1PmweZTEb9+vW5fPkyTk5OQHbv9cqVK9OlSxeysrL4448/SE9Pp1u3bjx58kSyxQqtrKwICQnBx8eHIUOGkJGRIUmOwsLExAQtLS38/f2ljpJvfH19MTQ0FAVhQRDyhCgKC4KQZ4pLC4nAwEC6du2qXOBMyJYzIuf169ecPHmSmjVrSpZlzpw5bNy4EW1tbezt7dmzZ4/yd0+fPuXs2bP06tWL8PDwzz7+5s2bmJqa8vDhQ1xdXfHw8ODZs2cAn53ymbPQ0fjx4wkKCuLGjRvK32loaGBgYJCLeydAds9qX19f+vXrh46ODj///DOtW7cmMjKSW7duMWPGDGrUqCF1zDxlZWWFXC4nODhY6igF2uXLl3F3d0dLSwtNTU2sra2ZOnUqJiYm9OrVi/Xr1+Pp6Sl1TEn5+/tTt25d6tevL3WUbyaTyWjfvj1r167l6dOnHDhwgFKlSjF48GB0dHQYM2YMJ0+eRC6XSx1V+D/Ozs5069aNsmXLAtkX+iIiIqhQoYJyQV+FQoGKisrfjqzNed2PHTv2Sb/+N2/eUKVKFcLDw4mNjaVXr16cP39e0lZOJiYmREREEB4ejo2NjWQF6sKgZMmSWFtbF8cWsqkAACAASURBVKsWEn5+fqJ1hCAIeUYUhQVByFN2dnZcuHCBhw8fSh0lTygUCtFP+DMSEhIwNzdHLpcTHh6Ojo6O1JFwcHBg/vz5zJs3T3lbWFgYI0eOZPv27dja2jJs2LDPnmCGhYXRsWNHli1bxrp160hMTOTQoUOkp6d/dsRwzjY6depE1apViY6OFv0C84BCoeDcuXOMGzeOatWqMWjQIEqWLMmBAwd48uQJv/76K61atSq0fYK/lqamJpaWlhw4cEDqKAVadHQ0Ojo6LFu2jNmzZ+Po6Ei9evVwdXVl+PDh9OjRg5CQEKljSsrf35+ePXsWub+dUqVK0bNnT3bv3k1SUhKrVq0iLi6Ojh07oq+vz6xZs7hz547UMYW/iIyM5O7du7Rr1w4dHR3l9+k/vT81NTXp0KEDISEhpKSkMGnSJADat2/PlClTOHPmDACDBw9m8uTJeb8T/6JFixacOnWKqKgoLCwsSElJkTpSgWVjY0NwcHCxmBVz//59bty4IYrCgiDkGVEUFgQhT7Vq1YqaNWsW2SLF9evXSUhIEEXhP3n06BFmZmaULVuW48ePf7I6eEFx7do13N3dGTt2LHZ2dri7uzNlypS/vb+2tjaJiYlkZGTQtGlTjIyMePDgAceOHQP+ebTwpk2bGDZsWJErrkjp/v37uLu706BBA4yMjLh58yZLliwhKSmJPXv20LNnT0qVKiV1TEn07duXw4cPKxdWEv6Xvr4+mZmZykX5xo4dy5w5cwBIT08nNjZWufBVcfTq1StOnTpVqFtHfIly5coxdOhQQkJCiI+Px8XFhYCAABo3bkzr1q1ZtWoVCQkJUscUgG7duvHTTz8p35Ofa/Hk6enJkSNHePr0qfK25ORkwsLC8PPz4/Tp02hra3Pnzh0WL14MUOA+J+vWrcupU6d49eoV5ubmJCYmSh2pQLK0tEQulyuPwYoyPz8/atasSfPmzaWOIghCESWKwoIg5Kmi3kIiKCiIRo0aSdoaoSCJjY2lQ4cO6OnpERwcXGAX89HR0eHt27esWrWKYcOGUblyZeXvbty48T8ninXr1kVfX5/Tp08D2VM9q1evzpUrV0hOTv7H0cI5C9gJ3+bVq1ds3rwZMzMz6tSpg5eXF0OHDuX+/ftEREQwYsQIypcvL3VMyVlbW/PmzRtOnDghdZQCq23btvTo0YNNmzZx7tw5tLW1qV69Oh8+fGDUqFHcvn0bNzc3qWNKJjg4GDU1NczMzKSOkm90dXVxdXXlypUr3LhxAwsLC1atWoWenh6WlpZ4enry7t07qWMWawYGBn/bKqJ169YsWbKE8ePHM3/+fG7fvg1kD0zIeUx6ejqlSpVCX18fyL6YWxAvHurq6nLixAnU1NQwNTXl/v37UkcqcDQ1NenatSu+vr5SR8lzvr6+2NraioEFgiDkGVEUFgQhz9nZ2XHmzBmePHkidZRcFxgYSPfu3aWOUSDcunWLDh060KRJE44cOaLsBVgQVa5cmYULF9KjRw/lbTmjfe/cufM/B9+GhoaUL1+eq1ev8vLlS8qUKUP9+vV5+PCh8oQzKipK9KTMZR8/fuTw4cMMGDCAqlWrMmPGDJo3b8758+eJiorCzc1NshXjC6rvvvsOc3PzIjs7IzcoFArs7Ozw8/OjTZs2yttLlizJ2rVrCQoKKjar2n+Ov78/3bp1K7Z98ps2bcqiRYuUC1PWqFGD8ePHU6VKFRwcHAgKCiIzM1PqmML/CQoKQlNTk5s3bxIQEMDdu3c5e/YsAC1btuT3339HT08PGxsbYmJiWLRoEVevXpU49T+rWLEioaGh1KpVC1NTU27duiV1pALHxsaGw4cPK2dlFUUvXrzg5MmTonWEIAh5ShSFBUHIc23btkVPT6/IFSnevHnD6dOnResIshdu6tixI8bGxhw8eLBQjI7NWa38r70JjY2N2b9//ycrgJcsWRJnZ2fu37/PwYMHAWjSpAl+fn48f/6cNWvW4OfnJ4rCuUChUHDx4kUmTJhAtWrVsLOzQy6Xs3fvXp4+fcq6deto27atGDXzD/r27Yuvr694P/6NP793/jzyUFVVlXLlyik/G4qjrKwsAgMDi3zriC+hoqJCx44d2bx5M4mJiezcuZP379/Tu3dvdHV1mThxIhcvXhT94iX27t07MjMzSUtLo2HDhgwZMoRdu3aRkZGBtrY25cuXx83NDXd3d8qUKcO1a9cwMDAo8N8hZcqUISAggPbt22NmZsb58+eljlSg9OrVi+TkZM6dOyd1lDwTEBBA+fLl6dChg9RRBEEowmRfciAjk8kU4oBHEIRvMWnSJK5cuUJERITUUXLNwYMHcXBw4MWLF6ipqUkdRzJnz56le/fu9OrVi23bthXagsqrV6/Yt28fT58+ZcOGDcTGxv7PaOegoCAWLFiAsbExNWrUIDg4mP3796NQKIrtqLrc8vDhQ7y9vdm5cyfR0dGYmpri6OhI//79qVChgtTxCpUnT56gp6fH2bNnad++vdRxCpwPHz6Qnp6u/KwqUaIEKioqn/y3uDp79izGxsYkJiZSpUoVqeMUSC9fvmTfvn14eXlx6tQp6tevj4ODA0OGDKFOnTpSxysWUlNTlRefP3z4QEREBJaWlqioqBAREcHMmTOV7Z4eP36Mnp4eAJmZmZQoUaLAF4T/LDMzk9GjR7Nnzx58fX3p2rWr1JEKDGNjY0xNTVm6dKnUUfJEv3790NDQwNPTU+oogiAUUjKZDIVC8Y9femKksCAI+cLOzo6TJ08WqUUzgoKC6NSpU7EuCIeHh2NhYcHAgQPZsWNHoS0IA3h7e7N+/Xq0tbUJDAz8bPsLKysr5s2bh0wmIzg4GDs7O0qVKiUKwv/Rmzdv2Lp1K+bm5tSqVYtt27Zhb29PXFwcJ0+eZNSoUaIg/B/o6urSrl075ah24VMHDx6kbdu2WFlZYWNjQ/fu3bGyssLCwgJLS0uaN2/Ojh07pI4pCX9/f9q0aSMKwv/gu+++Y/To0Zw8eZJ79+7h5OSEt7c3+vr6mJiYsHHjRl68eCF1zCLr2rVrmJiYMHXqVIYPH46qqirm5uaoqGSf1rZu3Zp3797x+PFjILsoHBcXB2TP+ilMBWHIzvz7778zduxYevbsWeRm3X0LGxsb/Pz8pI6RJ1JTUwkKCsLW1lbqKIIgFHFipLAgCPlCLpejp6fH7NmzGTt2rNRxvplCoaBmzZr89NNPjBs3Tuo4kggMDKRv376MGTOGlStXFroTrS+hUCiQy+WfHTkol8uVJ6HCl8vMzCQ4OBhPT098fX1RV1dn0KBBODo60v7/sXfnYTWt7R/Av7uipMhYKpI0SCpSHRRNSgO7ss2Z6sgsM+GYHVOGDJmnEJLmSSVNMhMlipOkUVJJ824/vz/8dL3nfQ/H0N5rV8/nulzveems+7uc2nuvez3rfn77rUV+HzFh165dOHHiBDIzM+nf6X+pr6/HwoULUVRUhJUrV6Jt27bgcrmN4zaqqqqgqqqKXr16MZxU8HR1deHo6Ij169czHaVZIYTg0aNHuHDhAi5duoQPHz7A2toaTk5OsLOzaxYjlYQdIQTV1dUYN24crKysMHfuXCxatAgNDQ1YtmwZVFVVISIignfv3kFfXx+pqam4dOkSPD09ERoa2uxXcRNCsGPHDqxbtw4nTpyAs7Mz05EYl5GRAQ0NDTx//hwaGhpMx2lSoaGh4HA4eP/+PaSkpJiOQ1FUM0VXClMUJTRERETg6OgIPz8/pqM0iefPn+Pt27etdp6wv78/2Gw2li1b1qIawoSQv82HPHbs2Fcbv7Qh/P2+NEyWLFkCBQUF2Nvbo7a2Fj4+PigoKICXlxeGDBnSYr6PhIGDgwNevXpFNyj6B23atMH+/fuho6ODvLw86OnpwcDAAEOGDMGQIUNgbm7eKhvCb9++xZMnT+g84Z/AYrGgp6eHffv2ITc3FyEhIejQoQNmzJgBOTk5uLi44ObNm3TO908ihIDFYkFSUhK1tbWQkpJCmzZtcOTIEcjLy+Po0aPIyMgAIQTdu3dHv379MG3aNHh4eODKlSvNviEMfP4ec3d3x+HDhzFr1izs2bOH6UiMU1dXh7q6OgIDA5mO0uSCgoJgbm5OG8IURfEdvaKlKEpgxo0bh7i4OLx7947pKL8sIiICqqqqUFFRYTqKwF24cAHjx4/Hpk2bsHXr1hbVyGOxWH87n9TUVLx584bBRM1bbm4udu7cCS0tLejp6eHu3bvYuHEjCgoK4O/vDwcHB4iLizMds0VSVVWFlpYWHSHxFRISEti4cSM4HA4AtKjXsZ8VFhaGHj16YODAgUxHadbExMRgZWWF8+fPo6ioCF5eXsjPz4eFhUXjE0apqalMx2xWampqAHxe5a+vr4/c3FyUlZUBADZu3AhxcXEcOHCg8ec4OzsbKSkpSEhIwIABA/7xmP/5dEBzMmfOHPj4+GD16tVYu3Ztq9/osCWOkGhoaEBwcDDYbDbTUSiKagVoU5iiKIExMjJC165dW8Qd/cjIyFa5Svj48eOYPn069u7dC3d3d6bj8M2XiywJCQlcuXLluy8cW/vFGQBUVFTg3Llzjastjx07Bg6Hg8zMTCQnJ2Pu3Lno0qUL0zFbBQcHB9oU/oqysjLcunUL8fHxLeJGZVMICwuDra0tbZA3ISkpKUyZMgURERHIy8vD8uXLERsbC21tbejo6GD37t2Ns2+pfxYXFwdnZ2c0NDSgTZs2MDc3R0REBG7duoWGhgYAwI4dO5CUlARPT08AgJeXF9LT09GjR49/PGZDQwNev34NfX19+Pv7N7v37gkTJiAkJAT79u3DvHnzGv8eWiM2m427d++2qD1L7t69i+LiYowZM4bpKBRFtQK0KUxRlMCIioq2iBESnz59QkJCAqytrZmOIlD79+/H3Llzcfz4cSxatIjpOL+MEPLVC6kvTZHJkycjISEBFy5cAJfL/eqxeDxe4+OtrRGXy0VkZCSmTJkCWVlZLF68GH379kVCQgL++usvbNq0CaqqqkzHbHUcHBzw+PFjvH79mukoQiU/Px/Ozs7gcDiYMmUK+vfvj1WrViEvL4/paIypqqpCTEwMHR3BR3JycnBzc8P9+/fx/PlzjBkzBl5eXujVqxfMzc1x5swZfPz4kemYQqewsBBVVVWNs/0tLCywePFibN++HbGxsSguLgYAWFtbN74Hm5mZQVJS8qvHFBUVRY8ePWBubo4JEybA1ta2cTO65mLUqFGIjo7GpUuXMGXKFNTV1TEdiRGGhobo1q0bQkJCmI7SZIKCgmBoaAg5OTmmo1AU1QrQpjBFUQLF4XAQGxvbrHfmjouLA4vFwogRI5iOIjDbtm3D8uXLceHCBbi4uDAd55elpKTAwsICISEh31xho6enB19fX4wbNw5iYmL/8+dcLrdxDjGLxUJlZSVCQ0Nx4sQJBAcH8/MUhMKTJ0+wbNky9OzZE6NHj8anT5/g7e2NgoICHDt2DEZGRq22US4MdHV10bt37xbxdEZT2r59O9TV1VFQUIDc3FysWLECHh4eGDRoEPbt24fKykqmIwrcl3m35ubmTEdpFTQ0NLBlyxZkZWUhMTERampqWLZsGWRlZRtXgbbWJt8XX1bvDh8+HBUVFQgKCsKaNWtga2sLERERGBgY4Nq1a1i4cCG2bNmChIQESEhIfPfxpaSksGvXLqSkpKCyshL9+/fHpk2bGkdVNAfDhg1DfHw84uLiYG9vj6qqKqYjCZyoqChGjx7dokZIBAUF0dERFEUJDG0KUxQlUCNGjICMjEyz/vAWEREBExOTb65CaSkIIVizZg02b94MPz8/TJo0ielIv6SkpATz5s2Dnp4epKWlMWDAgMbVR1/Tvn17tGvX7m+b0H3535KSEtTW1jYe49mzZ7h//z4SEhIwffp0rFu3jr8nxID8/Hzs3r0b2tra0NXVRVJSEtauXYv8/HwEBQWBw+H80IU5xT8sFgsODg7w9/dnOopQKSsr+9sKrIEDB2LOnDnYtm0bkpOTMXjwYNy5c4fBhIIXFhYGU1NTuqmRgLFYLAwbNgxHjhxBYWEhLl++DB6Ph3HjxkFeXh7z58/H7du3m914g6bw5YYil8tFcXEx9u7di3bt2qFPnz44c+YMlJSU4OLiAisrKwDAlClT4Orq+sN1+vfvj7i4OJw4cQJeXl7Q0tJCZGRkk54LP+no6CApKQnPnz+HpaVl46zl1oTNZiMmJgafPn1iOsove/HiBTIyMmhTmKIowflykfutX5+/jKIoqmn8/vvvxNramukYP4XH4xFlZWWyb98+pqPwXUNDA1m0aBGRkJAgkZGRTMf5JfX19eTQoUOkU6dORF1d/ZfO5/79+2Tt2rXk0qVL5OPHj3/7Mx6P1/jPDx48IEOGDCHv3r376VrCoqKignh7e5ORI0cSERERoqSkRNauXUtevHjBdDTqXyQkJBAWi0UKCwuZjiI0Tp48SRwdHUlERAR5//49GT9+PJkxYwYpKioihBDi5+dHUlJSGE4pODwej/Ts2ZMcPHiQ6SjU/ystLSUnT54kJiYmBADp06cPWb9+PcnIyGA6Gt95eHgQLy8vcu3atcbfmzhxIunbty95/vw5IYSQ+Ph4oqWlRR4+fNiktUtLS8n8+fOJiIgIGTt2LMnJyWnS4/NTbm4u0dTUJDo6OqSgoIDpOAJVVVVFJCUl//Y901zt2LGDqKmp/e3zJEVR1M/6/17uN/u9dKUwRVECx+FwEBMTg9LSUqaj/LCXL1/i9evXLX6ecENDA1xdXXH69GlERkY2rsRpjuLi4jBo0CC4u7tj3bp1ePr06S+dT3BwMCQkJGBlZQVpaWkAaNyI7j9HJSQlJSE7Oxtt27b9tRNgSENDA6KjozFt2jTIyclhwYIF6NWrF2JjY5GVlYWtW7dCXV2d6ZjUvxg6dCi6devWKsaZfA9CCFxcXDB27FgsXLgQ3bt3x4cPH7B69Wp0794dADB27Fjo6OgwnFRwUlNT8fbtW9ja2jIdhfp/MjIycHFxwc2bN/HmzRu4urri2rVrUFdXh6GhIQ4ePNjiNkjk8XgYOnQobt26hQcPHsDb2xurVq0CAAwaNAjt2rWDgoICgM8jJfr164fMzMwmzSAjI4NDhw7h3r17yMnJQb9+/bB7927U19c3aR1+UFBQQEJCAsTFxWFsbIzs7GymIwlMu3btYGVl1SJGJX0ZHUFHb1EUJTD/1jUmdKUwRVFNrK6ujnTq1ImcO3eO6Sg/zNPTk/Tu3btF38Gvq6sjkyZNIjIyMuTOnTtMx/lpb968IePHjycsFovMnDmzSVfOlJeX/8/v1dXVkerqauLv70+2bNlCunbtSvz8/Aghn1cX37p1q8nq89PTp0/JihUriLy8PBEVFSW2trbk8uXLpKqqiulo1E+aNWsWGTVqFNMxhMZ/r/D/8nvl5eWkvr6egUTM+vPPP4mmpibTMah/wePxSEpKClm+fHnj67ONjQ3x8fEhlZWVTMf7Zbdu3SKOjo6EkM/vp/7+/mT06NGksLCQlJSUEEVFRbJq1SqSk5NDHjx4QDp37kxu3rzJtzxcLpccPXqUdOrUifTv35/Ex8fzrVZT+vjxIzE3Nyfy8vLk2bNnTMcRmLNnz5LOnTs369fwwsJCwmKxSFJSEtNRKIpqIUBXClMUJYzatGkDNpsNPz8/pqP8sIiIiL/tcN3S1NbWYty4cYiJiUFcXBwMDQ2ZjvTDqqursWXLFmhoaCA7Oxu3b9/G6dOnm3QX5w4dOgAAEhMTsXv3bixevBh6enoYNWoUtmzZgqKiIvj6+sLR0RExMTFwcXGBu7s7+vTpI5QbwRQWFmLv3r0YOHAgtLW1ERsbi5UrVyI/Px+hoaGYMGEC2rVrx3RM6ic5Ojrixo0bKC8vZzqKUFixYgXCwsIAfP7eX7hwIRQVFSEjIwNdXV3Ex8cznFCwQkNDYWdnx3QM6l+wWCzo6Ohg9+7dyMnJwfXr19G9e3fMnj0bsrKymD59OqKjo7+5eaow+pKXEIKUlBRkZWWhTZs2jU+mREdHo3PnzoiNjcXdu3exZs0auLq6wsPDAyYmJnzLJSoqitmzZyMjIwMGBgYwMTHBtGnTUFRUxLeaTUFaWhphYWH47bffYGxsjHv37jEdSSBsbW1RVlaGpKQkpqP8tJCQEHTt2hW//fYb01EoimpFaFOYoihGcDgcXL9+HR8/fmQ6ynerrq5GXFwcRo0axXQUvqiqqgKbzcb9+/cRHx/f7B6fJoQgICAAmpqaOHToELy8vHD79m2+NrYLCgqwatUq1NTU4OjRo7hw4QLu3LmDgwcPwtTUFCwWC8OHD0dwcDDi4+MbdzcnQrBpUFVVFXx8fGBtbQ0FBQXs378f1tbWSE9Px4MHD+Dm5tb4OD3VvJmZmaFdu3YIDw9nOopQyMrKarw5s27dOnz48AEZGRng8XiYNGkSNm3ahNTUVIZTCsb79+9x+/ZtOjqimREVFYW5uTnOnDmDwsJCnDx5Eh8+fICNjQ169uyJZcuW4fHjx0LxXvMtZWVlcHZ2RnFxMYYNGwZzc3P4+PggPj4ed+7cgYKCAmJjY1FbWwtVVVUEBgbi2LFjCAoKwsyZMwWSsVu3bjh9+jQSExORkpICdXV1eHl5CXXzXVxcHFeuXAGbzYaZmRlu3LjBdCS+69q1K4yMjJr1RtZBQUEYM2bMv26ATFEU1ZRoU5iiKEZYWFhAQkICoaGhTEf5bvHx8WhoaICZmRnTUZpcRUUFrK2t8eLFCyQkJKBfv35MR/oh6enpsLS0xPjx4zF27FhkZmZixowZEBHh79vc+PHjcfLkSURHR6O6uhqKiopo27Zt4/xBQgjatm0LJSUlAEBdXR3ExcUZW2nO4/EQGxuLmTNnQlZWFrNnz0aPHj0QExOD7Oxs/Pnnn83uvz3179q2bQtbW1v4+/szHUUo1NXVoaamBsDn146JEydCTk4O9fX1WLt2LWpqapCVlcVwSsGIjIxEx44dMXToUKajUD9JUlISEyZMQEhICPLz87F27VokJydj0KBB0NLSwvbt2/HmzRumY/6jyspK3Lx5Exs2bACXy4WjoyMyMzNhZ2eHp0+fQltbG2lpaRAXFwfw+SkdSUlJKCoqCjzrsGHD8OjRI2zYsAGrVq2CoaGhUK/CFRMTw6lTpzBnzhzY2NggICCA6Uh8x2azERQUJPQ3Q/7Jp0+fEB0dDTabzXQUiqJaGdoUpiiKEeLi4s1uhERkZCSMjY0hJSXFdJQmVVpaCgsLCxQUFCAxMREqKipMR/puZWVlWLJkCbS1tSEiIoLU1FR4eHigY8eOAsvg7OyMnTt3YsyYMUhOTgbweUQK8Plx3w8fPiAvLw/Tpk2DjIwMTE1NAUCgFy3p6elwd3dH7969MXLkSBQWFuL48eMoKirC6dOnYWpqyvcGOsUsR0dHREREoLq6mukojJORkcGlS5cQGhoKQgjevn0L4PPPbXl5Oerq6lrNuJTQ0FBYW1tDTEyM6ShUE+jWrRvmz5+P27dv4+XLlxg/fjxOnz6N3r17Y8SIEThx4oRQbfKroKAAJSUlpKamwtXVFRYWFvD29kZycjIOHDiAq1evolu3bkhMTAQAxkd3iYmJYcmSJXjx4gX69u2L3377DXPmzMGHDx8YzfU1LBYLu3fvxsaNGzFu3DicPn2a6Uh8xWaz8fr1a6SlpTEd5YdFRUVBVFQUFhYWTEehKKqVoVeAFEUxhsPhICIiAp8+fWI6ynf5Mk+4JXn37h1MTU1RVVWFhIQE9OzZk+lI34XH4+HUqVNQU1NDcHAw/P39ERkZCQ0NDUbycDgc3L17F+Li4sjJycGJEyfw559/YtasWTAwMMCMGTNQWlqKFStWNM6K4/fF7bt37+Dp6YnBgwejf//+iIyMxJIlS5CXl4eIiAhMmjQJkpKSfM1ACY9Ro0aBy+UiOjqa6SiMO3LkCObOnYv79+9DRkYGYWFhePv2Lf766y8MGzYMVlZWMDY2Zjom39XX1yMyMpKOjmih+vbtiw0bNiAzMxN37tyBtrY21qxZAzk5OYwdOxYBAQGora1lLF9tbS14PB769OmDNWvWQEFBAfPnzwcA9OrVC+Li4qivr8fHjx+Fbo6vgoICLl++jKioKNy8eRPq6uo4c+YMeDwe09H+B4vFgru7Ow4dOoRZs2Zh7969TEfiGxUVFWhpaSEwMJDpKD8sKCgIlpaWreaGJEVRwoP1PSuVWCwWaY6PYVAUJdxqamrQrVs3nDx5EhMmTGA6zjdlZWVBRUUFqamp0NLSYjpOk8jLy4OFhQXat2+P69evo0uXLkxH+i63b9/GokWLkJ6ejrVr12Lp0qWQkJBgOhaAz4/COjk5ISgoCOvXr4empibExMRgYWHRuDkdgMaVWrGxsQgODgaHw8Ho0aN/uX51dTWCg4Nx/vx5REZGQlZWFk5OTpg6dWqL+b6lft6YMWPQpUsXnDlzhukoQqGhoQEiIiKNN2i+zLiXlpZmfEWiIMTHx8PMzAzv3r1rNq//1K+pr69HVFQULly4gMDAQEhISGD8+PFwcnLCsGHD+P7ESFZWFlgsFpSVlRt/b+PGjZCQkMCSJUvg5uaGhIQEqKqqNs6GvXnzJgYPHgxpaWm+ZvtZtbW18PDwwNatW6GnpwcvLy9oa2szHesfXb58GVOnTsWqVauwZcuWFvk6t27dOkRGRuLBgwdMR/luXC4XsrKy2Lt3L6ZPn850HIqiWhAWiwVCyDdf7OlKYYqiGCMhIYHRo0c3ixESkZGRUFRURP/+/ZmO0iSys7MxfPhwdOnSBTdu3GgWDYGCggJMnz4dw4YNg6qqKjIyMrBmzRqhaQgDQPv27eHl5QU9PT18+PAB48ePh6OjIzp06ICSkhL4+Phg586dMDc3h7u7O8aNG9e4S/jP4vF4iI+Px++//w45OTm4uLigurc3zQAAIABJREFUS5cuiIyMRE5ODnbu3EkbwhSAzyMkgoODweVymY7CqIaGBhw5cgRsNhtjxozBkSNH8OHDB3To0AEdOnRokY2SfxIaGoqhQ4c2i9d/qmm0adMGtra2uHTpEoqKirB//35kZWVhxIgR6NOnD9auXYvnz5/zpfbq1avh4OAAa2vrxhXBwOfPglVVVRAXF0dJSQk+fPjwt9n2pqamQtsQBj6PQ1u7di3S09PRuXNnDBo0CEuXLkVFRQXT0f7HxIkTERwcjL1792LevHlCvVnez2Kz2Xj48CFyc3OZjvLdkpKSUFZWRp/aoCiKEbQpTFEUozgcDsLDw1FZWcl0lG+KjIzEqFGjWkSzIDMzE8bGxujTpw+uX78u0Pm7P6Ourg67d++Gmpoanjx5gvj4ePj4+DCy0cz36NGjBxISEpCUlIRp06YBAMLCwmBra4srV65ARUUFZ86cweDBg6GtrY2HDx+iW7dujRdn3/v4aUZGBtatW4c+ffrAzMwMb9++xeHDh1FUVIRz587BwsKC7mBN/c3o0aNRXl6OhIQEpqMwatu2bQgKCgKHw4GdnR02b94MTU1NHD9+HFVVVUzHE5jQ0FDahGjFOnTogOnTpyM6Ohpv377FwoULER4eDk1NTejp6WHfvn0oKChoklq+vr4IDw/H7du3cfPmTYSHh+Ps2bMAPj/BUFpaClNTU5SVleHu3bt49OhRs9sYTVlZuXGclb+/PzQ0NODr6yt0m55ZW1sjOjoaly5dgpOTE+rq6piO1KT09PQgLy+P4OBgpqN8t6CgIBgZGaFr165MR6EoqjUihPzrr89fRlEU1fQqKyuJpKQk8fPzYzrKV9XU1JD27duTa9euMR3llz19+pTIysoSOzs7Ul1dzXScfxUeHk7U1NRI586dyZEjRwiXy2U60g/ZuXMnycvLI4QQwuFwiJmZGSGEkAcPHhAlJSVy9+5dQgghHz9+JDwej1RWVpK0tLSvHq+kpIQcPHiQ6OvrEwBkwIABZPfu3Y01KOrfmJmZkQULFjAdg1Ha2tokJSWl8f8fPXqUjBgxgqioqBB9fX0SGRlJeDwegwn579WrVwQASU1NZToKJWRSU1PJ6tWrSc+ePYmIiAixtLQk3t7epKKi4qePmZiYSJYtW9b4Hv7nn38SDw8P8urVK/Lo0SPSo0cPMnXq1Mavz83N/eXzYFJlZSVZs2YNadOmDbGwsCAZGRlMR/ofKSkpRFZWllhbW5PKykqm4zSpOXPmEEtLS6ZjfBcej0eUlZXJnj17mI5CUVQL9P+93G/2e+lKYYqiGCUpKQlbW1uhHiGRlJSE2tpamJubMx3llzx48AAmJiYYPnw4rl27JlRjF/7bq1evMHr0aNjZ2WHkyJF4+fIl5syZ0+xWvq5cuRLy8vIAgKtXr0JeXh66urrgcDi4dOkSDAwM0NDQgMePH2P79u1QU1ODra0t7ty58z/H4nK5IIQgICAAw4cPR0pKCp4+fYrly5c31qCof+Pg4ICAgACh3BBJUBQVFfHs2bPGVcHv3r3DgAED8OTJE+zduxezZs3i2yP0wiIsLAxKSkotZiQS1XS0tLSwfft2ZGdnIzY2Fr169cLChQshKyuLKVOmICIi4rtH0DQ0NKCurg7y8vKoqqqCr68vnjx5gvDwcKSmpsLR0RF3797FoUOH4O3t3fjvKCgo8PMU+U5SUhLbtm3D06dPQQjBgAED8McffwjVkwg6OjpISkrC8+fPYWlpibKyMqYjNRk2m42bN2+ivLyc6Sj/Ki0tDa9fvwabzWY6CkVRrRRtClMUxTgOh4PQ0FBUV1czHeUfRUREYOjQoUI/ZuFbkpKSYGZmhtGjR8PHxwdt27ZlOtI/+vTpE9zd3dG/f39UVFTg8ePHOHToEDp37sx0tF/yZYd3T09PFBQUoLi4GEOGDAEAiIqKYvjw4ejatSsUFRWxYcOGf5wBLCYmhk6dOiEmJgYeHh7Q0dER6DlQLYO9vT3y8vKa1SY8TW3Dhg3YuXMnXFxc4Orqit27d8Pa2hrt27eHkZER/vrrL2hqajIdk6++jI5oCSORKP4QERHBiBEjcOLECRQWFsLb2xtVVVVgs9lQUFDA4sWL8e7du38cj9DQ0AA2mw1nZ2dMmTIF3bt3h62tLW7dugVTU1MYGRnh7Nmz8PHxwbFjxxqbzFwut9nd/P0WDQ0NREdH49y5czh16hT69++PkJAQpmM16tu3L5KSklBaWgoTExMUFRUxHalJmJqaQkJCApGRkUxH+VeBgYHQ0tKCiooK01EoimqlaFOYoijG2djYgMvlIioqiuko/+jLPOHmKiYmBlZWVnBycsLp06chJibGdKT/QQjBxYsXoa6ujosXL+L8+fO4efOm0O7g/aPExcVRVVUFAwMDbNq0CSdPnkROTk7jHOGnT5/Cx8cHM2fOBJvNhpSUVOO/W1NT0/jPIiIitIlD/RJFRUUYGBg0u3mdTcnAwAAPHz6EtbU1NDQ08OjRI9jY2DT+PLZp04bhhPxVUVGB+Ph42NnZMR2FaiYkJCQwduxYBAQEoLCwEFu3boWDgwM6der0P+9J9fX1MDU1RdeuXbFo0SJ06tQJrq6usLGxwaFDh+Dm5oYJEyYAAPr374+ePXsiJycHAITy88mvYrFYmDhxIl68eAF7e3vY29uDzWYjOzub6WgAAAUFBSQkJEBcXBxGRkZCk+tXiIuLw9raGoGBgUxH+VdBQUF0lTBFUYyiTWGKohgnJSUFa2troRwh8fbtWzx79gzW1tZMR/kpoaGhsLOzw7x583D48GGIiAjfy/6jR49gbGwMFxcXuLi44Pnz5xg/fnyLa35KSkriypUrmDNnDiZOnAgFBQWIiooiKysLy5cvh5WVFRwcHBpXRX/48AEHDhyAsrIynj59ynB6qiVxcHCAv7+/0G2AJCglJSWorKzEhAkTMG/ePMjKygIA9PX1kZmZyXA6/ouJiYGYmBhMTU2ZjkI1Q507d8asWbMwYsSIf7yBIioqCm1tbZw6dQp6enqYPXs2uFxu42rgT58+Yc+ePYiMjMSBAwcQExODQYMGCfo0BK5Dhw7Yt28fHj58iOLiYmhqauLPP/9sfJKISV26dEFMTAyUlJQwbNgwpKenMx3pl7HZbISHhwv1Rnq5ubl4+PAhbQpTFMUo4esOUBTVKnE4HAQHBwvFh+P/FBkZCTk5uWb5qP7Vq1fh4OAAd3d37Nq1S+iarMXFxZg9ezYGDx6M7t274/nz59i8eTPat2/PdDS++XLhW1dXB1FRUbx//x5ubm7Q19fHjBkz0L17dwBAbGwsjh49iidPnmDKlCkYO3YsYmJimIxOtSAODg7IzMxs8XNzv2bBggUYOnQoRowYATs7O4wZMwaWlpbIyMiAs7MzDAwMUFxczHRMvgkNDYW5ublQz5Wnmq+qqipkZWXBx8cHhBCUlJQgPj4ebm5usLOzw7Jly8DlcrFnzx4cPnwYFy9ehJmZGdOxBUZXVxdJSUk4dOgQ9u7dCx0dHdy4cYPpWJCWlkZYWBgMDQ1hbGyMe/fuMR3pl9jY2KCqqgrx8fFMR/mq4OBgyMvLQ09Pj+koFEW1YqzvWSXCYrFIa11NQlGUYHz8+BHdu3fHtWvXYGtry3ScRo6OjujYsSPOnDnDdJQfcu7cObi4uGDHjh1Yvnw503H+hsvl4siRI1i/fj169OgBT09PjBw5kulYAldbWwt7e3v069cPbm5uUFJSAgAkJibC09MTGRkZ8PPzg7q6Os6fPw83Nzc8e/YMcnJyQtfgp5qf/v37Y/LkyVi7di3TUQQuKysLv//+Ozp06IB169ahZ8+e6NSpE1RVVeHn5wdFRUV07969Rc02/YLH40FBQQGbNm2Cq6sr03GoFoYQAhaLhYcPH2LRokWQkZFBSkoKzp49i5EjR8LOzg6dO3eGt7c3ysvLUVNT07hSvzUqKSnBmjVrcOLECUyYMAF79uxhfONYLpcLV1dX+Pr6IigoqFlvsmxhYQENDQ0cOnSI6Sj/yNLSEn379oWXlxfTUSiKaqFYLBYIId+8cKQrhSmKEgodOnSAlZWVUI2QqK+vR0xMTLObJ+zl5QVnZ2ccOHBA6BrCsbGxGDhwINatW4cNGzbgyZMnrbIhDABlZWXQ0tLCrFmzGhvCFRUV0NXVxZw5czBw4EBwOByUlpZi6tSpeP78OXr06EEbwlST+DJCojXq06cPYmNjMXz4cEyfPh2JiYlo27YtREREoKioiB49erTIhjDweVxPYWGhUN18pVoOFosFHo8HPT09BAQEYOnSpViwYEHj+/ycOXOQnZ2NiooKdOzYsVU3hIHPYxuOHTuG27dvIyMjAxoaGvD09GwctcEEMTExnDp1CrNnz4aNjU2znj/PZrMRHBwslKOSysvLERcXR0dHUBTFONoUpihKaHA4HAQGBgrN/K/k5GRUVlY2q6alh4cHFi1ahNOnT2PevHlMx2n05s0bjBs3DhYWFjA0NMTLly+xePHiFruhU2lpKdLS0ho3rvonsrKy2LFjB/r16wfg82OEO3fuhLS0NCwsLODt7Q0NDQ3ExsaCEIKuXbsCwDePSVHfy9HREY8ePcKbN2+YjsIIQgiWLl2K8PBw7N27F6tWrUJFRQXatm3LdDS+Cg0NxcCBA6GgoMB0FKqF+rJ3wZfV9nv27MHDhw9RXl6OEydOQFxcHOLi4gynFC6Ghoa4f/8+/vzzT6xfvx6DBw9GcnIyY3lYLBY8PDywYcMGjBs3rtk9LfcFm83G27dv8fjxY6aj/I+IiAhISEjAxMSE6SgURbVytClMUZTQGD16NCorKxEbG8t0FACf5wkbGho2bvwlzAgh2LhxI9zd3eHj44Pp06czHQkAUF1djU2bNkFDQwO5ubm4e/cuTp482Tg7tyWpq6tDUFAQOBwO5OTkYGpqioiIiG+uUPnP1YgvX778n3nKKSkpKC8vB4vFavza169fIzExEfv37+fPiVCtwsCBA6GkpNQsdmdvajU1NY0r7pWUlJCcnIwePXpATk6uxTeFw8LC6CphqknU1tb+6018ExMTzJ8/H8OGDYO1tTVevXoFX1/fr/6cCeOKTkERFRXFggULkJGRgQEDBsDIyAguLi54//49I3lYLBbWrFmDgwcP4vfff8fevXsZyfErevXqhYEDBwrl+1xQUBCsra3pDRKKohhHm8IURQkNGRkZjBw5UmhGSERERMDa2prpGP+KEIKVK1dix44dCAgIwPjx45mOBEIIrl27hn79+uHo0aM4duwYbt26BX19faajNSlCCO7evYsFCxZAXl4eEyZMgIiICK5du4b8/HzY2dl997gHcXFx+Pn5oaKiAhUVFXBxcYG+vj4mT56Mqqoq7Nu3Dy4uLpgwYQK2bt2KV69eoaSkhM9nSLVULBYL9vb2rXKExB9//IGIiAhUVFQ0/t7ixYuRlpYGaWlpBpPxV0FBAR48eAA7Ozumo1DN3MqVKzF16lTY2toiPj4ehYWFX/3aTZs24ebNm9i5cyeePn2KTp06/ePX1dXV4dGjR5g5cyZCQkKE5qkxQZOTk8P58+cRGxuL27dvQ11dHcePHwePx2Mkz9y5c3Hx4kWsWrUK69ata3aNezabjaCgIKZj/E1dXR3Cw8Nhb2/PdBSKoii60RxFUcLlzJkzWL58OQoLCxkdLZCfnw8FBQXcu3dPqBuZPB4PCxcuxNmzZxEUFAQLCwumIyEtLQ1ubm5ITEzE4sWLsW7dOnTo0IHpWE0qOzsbFy5cwPnz55GZmQljY2NMmzYNHA4HMjIyP33cCRMm4K+//kJ1dTU6dOiAHTt24O3btzh69Cju3buH3r17Y8GCBVi0aFETng3VWiUkJMDU1BQFBQUtcvX+13Tq1AkqKipQUlLC3LlzMWTIEEhISLTYOcJfnDp1Cu7u7igsLGx8xJ+iftSjR48wefJkJCcn48aNG0hJSUG3bt1gY2MDNTW1v33tl43nvgePx0NERATOnTuH4OBgSElJYfz48XBycsKQIUNa5Tz9uro67N+/H5s2bcKAAQPg5eWFQYMGMZIlPDwcHA4HM2bMwKFDh5rNa0hKSgoGDhyIrKwsKCsrMx0HABAVFQVbW1sUFxf/0mdGiqKof0M3mqMoqtlhs9n4+PEj4uPjGc1x/fp1dO3aFXp6eozm+BYulwtnZ2ecP38e169fZ7whXFpaCjc3N+jq6qJt27ZITU3Frl27WkxDuLy8HCdPnsSIESOgrKwMb29vODk5ISsrCwkJCfj9999/+cP9lStXcOrUKVy8eBHJycmQlZVFfHw89PT0EBUVheXLl+PWrVvIyspqXK1z9+5dHDp0CFVVVU1xmlQrMmzYMHTp0gXBwcFMRxEoKSkpPHjwAIsWLcLWrVthYWGBgwcP4s2bNy16ZndYWBhsbGyaTTOHEk5lZWXg8Xjo3Lkzxo0bB3t7e9TW1uLKlSv/M6P8Rxq5IiIisLW1ha+vL4qKirBr1y68ePECRkZG6Nu3L9avX4/MzMymPh2h1rZtW6xcuRLPnz+HvLw89PX1sXDhQpSVlQk8i42NDaKiouDj44MpU6Y0m5XcOjo6UFJSEqr3uaCgIJiYmNCGMEVRQoF+KqQoSqh07twZZmZmjI+QiIyMhJWVldBePNfV1WHy5MkICQlBbGwsjIyMGMvS0NCAEydOQE1NDWFhYQgMDER4eDjU1dUZy9RU6uvrERoaigkTJkBWVharVq2ClpYW7ty5g4yMDPzxxx9NvvJER0cHurq6YLFYCAwMRE5ODnbt2gUTExNoa2vj5cuXaGhoaPzz7du3w8/PDzo6OsjNzW3SLFTLJioqijFjxjTr3eV/BpfLRW1tLUaMGIG4uDicPXsWubm5MDY2hp6eHubMmdPsHpH+N7W1tYiKiqKjI6hfNmTIEPTq1QunT58GAOjr68Pc3BxcLhcxMTEAPjeO09LSfrpGx44d4ezsjNjYWLx58wazZ89GQEAA1NXVYWBggIMHD+Ldu3dNcj7NQa9eveDv74+QkBCEh4dDQ0MDFy9eFPjrlJGREeLj4xEbGwt7e/tmcTOaxWJhzJgxQjNCghCC4OBgsNlspqNQFEUBoE1hiqKEEIfDgb+/P2MrtrhcLqKiooR2nnBNTQ3Gjh2L+Ph4xMXFYfDgwYxluXXrFgwMDLBkyRIsW7YMz549+6E5usKIEIIHDx7Azc0NCgoKGDt2LLhcLq5cuYKCggIcPnwYhoaGAjlHKSkpjB49unEjkvbt26OyshLv379HYGAgJk6ciNWrVyMuLg5OTk4wNjZGTk4O33NRLYejoyNiYmLw8eNHpqMIjKioaON4IkII1NXV4eHhgZycHJw9exYqKirN+jXsn8THx6O2thaWlpZMR6GaOQkJCdjb2yMlJQWJiYkAgMGDB0NNTQ0XL15EQ0MDtmzZ0mSrenv27ImVK1ciNTUVT548gYmJCXbs2AF5eXnY2NjAx8cHlZWVTVJL2NnY2CAtLQ1z5syBi4sLzMzMkJ6eLtAMOjo6SEpKQnp6OqysrBhZtfyj2Gw2EhIS8OHDB6aj4NGjR8jNzaVNYYqihAZtClMUJXTs7e1RUlLSeLEhaPfu3UN5eblQXjxXVlbCzs6u8WJswIABjOTIz8/H1KlTYWxsjH79+iEjIwOrV69u1rso5+TkYPv27dDU1IS+vj4ePHiALVu2oKCgANeuXQObzf7qjun8oq2tjfXr12PXrl3Ys2cP5s2bB3NzcwwZMgQ3b96EmpoaDh8+DADYsGEDDh48SJvC1A8xNzeHuLg4wsPDmY4iMGPHjm18CuS/m7+6urpYsWIFE7H4KiwsDMOHD28x43wo5rBYLLi6uqJDhw6IjIzE9evXAQCjRo1CeXk5iouLsW3bNjg6OjZ5bW1tbezatQs5OTmIioqCnJwc5syZA1lZWUybNg1RUVEtegQMALRr1w4bN25EWloaxMXFoaOjg9WrVwu0Ma6qqopbt27hw4cPMDExQVFRkcBq/4zhw4dDWlpaKN7nAgMDMWjQIPTs2ZPpKBRFUQDoRnMURQkpc3Nz9OvXD4cOHRJ47T/++APXr1/HvXv3BF77W8rLy2Fra4uCggLcuHEDvXv3FniG2tpa7N+/H1u2bIGamhoOHjyIYcOGCTxHU/n48SOuXbuG8+fPIy4uDn369MHUqVPh5OQEFRUVpuMBABITE7F9+3ZoaWmhffv2GDduHDQ1NVFdXY127drB3NwcgwYNwu7du5mOSjVTkyZNAo/Hw5UrV5iOIjA1NTWoqakBAIiJiUFUVBSioqICv/EjCIQQqKioYOHChViyZAnTcagWoqqqCseOHUNiYiLat28PZWVlREZGIiAgAPLy8gJbbV9dXY2QkBBcuHABERER6Nq1KyZNmgQnJycMHDiwxa36/0+EEPj7+8PNzQ0iIiLw9PSEvb29wM65pKQE1tbWKC0tRXR0NCOfS7+Xk5MTampqGB9Pp62tDQ6Hg/Xr1zOag6Ko1uF7NpqjTWGKooTSkSNHsHnzZuTl5Ql8rq++vj6sra2xefNmgdb9lpKSElhZWaGyshIxMTFQUFAQeIawsDAsXrwYZWVl+PPPP+Hs7AxRUVGB5/hVXC4X0dHROH/+PAIDAyEhIYEJEyZg6tSpQrvDeW1tLcTFxZGamoo1a9Zg//79UFZWhoiICFavXo02bdpgy5YtTMekmilfX1+4uLiguLgYEhISTMfhu+LiYhw5cgQxMTEoKSlBfX09ampqICkpCQ6Hgz/++KNZP/Xw354/fw5NTU1kZmZCVVWV6ThUC8LlcvHXX3/hwIED6N27N/T09GBmZsZYnvfv38PX1xcXLlzA7du30a9fPzg5OWHy5MlC3bD8VZ8+fcLmzZuxb98+jBw5EgcPHhTYje2KigrY29sjIyMDUVFR0NTUFEjdH3X16lXMnDkT79+/Z+x97vXr1+jTpw9SUlKgo6PDSAaKolqX72kK0/ERFEUJJUdHRxQVFSE5OVmgdd+9e4cHDx4I1TzhwsJCmJiYgMvlIj4+XuAN4czMTNja2oLNZsPGxgaZmZmYNWtWs2oIE0Lw+PFjLF26FIqKimCz2aiursaFCxdQUFCAI0eOYOjQoULZEAbQuHqxuLgYpaWlUFFRgYiICN68eYPi4mK6gzX1S6ytrVFfX9+4SVRLxuVyMX36dMjKyiIhIQHPnj1DZmZm4+PoKSkpWLlyJdMxm1RYWBhUVVVpQ5hqEv+5UEhMTAzq6uo4fPgwVqxYwWhDGAC6du2KefPmITk5Ga9evcLEiRNx9uxZKCsrY/jw4Th+/DhKS0sZzcgPUlJS2LVrFx4/foxPnz6hf//+2LRpU+PTEPwkLS2NsLAwGBgYwNjYWOiesvti1KhRqK+vR2xsLGMZgoKC0Lt3b2hrazOWgaIo6r/RpjBFUUJJVlYWw4cPx9WrVwVaNyoqCp06dYKBgYFA637N27dvMWLECEhKSuLmzZvo3r27wGpXVFRg1apV0NLSQnV1NVJSUuDp6YlOnToJLMOvysvLw65duzBgwAAMGjQIt2/fxvr161FQUICAgAA4Ojo2ixWBX5rVxsbG+PjxI9hsNpydnbFo0SJkZ2dj+vTpDCekmjNpaWmMHDkSAQEBTEfhu7q6Ojx8+BCzZ8/+nz/r1asX3NzccPv2bQaS8U9oaCjs7OyYjkG1AL6+vpg/fz6awxOkKioqWL9+PTIyMnD37l3o6upi3bp1kJOTg6OjI/z9/VFbW8t0zCalpaWF+Ph4nDhxAl5eXtDS0kJkZCTf60pISMDX1xdjxoyBubk5o43Xr5GWloaZmRmCgoIYyxAYGAg2my20CxAoimqdaFOYoiihxeFwcO3aNfB4PIHVjIiIgKWlpVCsgv3rr79gbGwMOTk5REdHC6wZy+PxcP78eaipqeHy5cvw8fHBjRs3oKWlJZD6v+rTp0/w9vaGhYUFevbsiSNHjsDR0REZGRm4ffs25s2bhy5dujAd86e0adMGT58+xZAhQyAvLw97e3t4e3uja9euAv05oVoeBwcHBAUFgcvlMh2Fr8TExGBqagoHBwfcuXMHHz58wKdPn/Dp0ydER0fD3d0dM2fOZDpmkyktLUVSUhJtClO/7PTp05g0aRKUlZWbVVOLxWLBwMAABw4cQF5eHgICAiAhIQEnJyfIycnB1dUVCQkJLeY9lMViYerUqXjx4gWsrKxgY2MDDoeDt2/f8rWumJgYTp06BVdXV1hbWyMwMJCv9X4Gm81GcHAwI/+tv2ygzWazBV6boijqmwgh//rr85dRFEUJVl5eHgFAkpOTBVKPy+WSLl26kDNnzgik3rekp6cTeXl5YmlpSSorKwVW9/79+2TIkCFEQkKCbNiwQaC1fwWXyyXXr18nU6ZMIZKSkqRjx45k1qxZJCEhgTQ0NDAdj+94PB4hhLSKc6X44927d0RERITcvHmT6Sh819DQQBYvXkzk5OSIlJQUkZWVJZ06dSJWVlbk6tWrTMdrUpcvXybS0tKktraW6ShUM7Z//34iIiJCjhw5wnSUJvPx40dy7tw5MnLkSCIiIkJ69epF3N3dybNnz5iO1qTu379P9PX1Sfv27cnu3btJXV0dX+vxeDyybds2IioqKhSfp//Tl+uKO3fuCLz2uXPnSKdOnUh9fb3Aa1MU1Xr9fy/3m/1eutEcRVFCzcjICIaGhtizZw/fa927dw+GhobIz89Hjx49+F7va1JSUmBpaYmhQ4fiypUrAhlv8O7dO6xduxanTp2Co6MjPDw8msWmLE+fPoW3tzd8fHxQXFwMa2trTJ06FaNHj24VG2YRQhpXbPF4PNTU1CAqKgr29vYMJ6OaI1NTU2hra8PT05PpKHxDCEFhYSG6d+8uFE+E8NvUqVNRU1Mj8FFMVMtACMG2bduwceNGnDt3DlOmTGE6El/k5+fj8uXLuHDhAh4/foyBAwfiSSS8AAAgAElEQVTCyckJkyZNYvTzYFNpaGjAyZMn4e7uDnl5eXh5eWH48OF8rXnkyBEsWLAAHh4eWLJkCV9r/QhDQ0OYmZlh+/btAq07duxYSEpK4vz58wKtS1FU60Y3mqMoqtnjcDjw8/MTyPy6yMhI6OrqMnoBcPfuXZiamsLCwgJXr17le0O4vr4enp6eUFNTw+3btxEdHQ0/Pz+hbgjn5+fDw8MDOjo60NHRQUJCAtzd3ZGfn4/g4GCMGzeuVTSEAfztEV4RERHcuHEDY8eOFchNFKrlcXBwQEBAQLOYF/qzioqKoKCggFmzZuHRo0eoqqpqsefb0NCAiIgIOjqC+imEEKxatQpbtmyBn59fi20IA4C8vDyWLl2KR48eIS0tDaNGjYKnpycUFRVhaWkJb29vVFRUMB3zp4mKimL27NnIyMiAgYEBTExMMH36dBQVFfGt5ty5c3HhwgWsXLkS69atE5rXWTabLfC5wtXV1YiMjKQ37CmKEk7/tpSY0PERFEUx6M2bNwQAuXfvHt9r/fbbb8Td3Z3vdb4mPj6eSElJERcXF8LlcvleLzo6mmhqapKOHTsST09Pvj9S+Cs+ffpEzp8/TywtLRsf81yzZg15/vw509GEzuXLl4mYmBhZs2ZN41gJivoeOTk5BAC5f/8+01H4Ji8vjygrK5Pr168TLS0tMnHiRBIbG0tqamoE8rorSLdu3SIsFosUFRUxHYVqZhoaGsicOXOIpKQkiYqKYjoOIxoaGkh8fDyZNWsW6dixI2nXrh2ZNGkSCQsLE+rPS98jMTGRDBgwgHTs2JEcPnyYr699YWFhREJCgsybN08oRlylpaURACQzM1NgNUNCQoi4uDipqKgQWE2KoihCvm98BF0pTFGUUOvVqxcMDQ3h5+fH1zolJSW4d+8eRo0axdc6X3P9+nWMGjUKM2fOxPHjx/n6WHN2djbGjh0LS0tLDBs2DC9fvsSiRYvQpk0bvtX8GQ0NDYiJicH06dMhKyuLefPmQVFRETdu3MDr16+xbds2aGhoMB1T6EyYMAHBwcHYt28f5s2bh4aGBqYjUc1Ez549MXjwYAQEBDAdhW/q6+shIiICS0tLpKamYtKkSdi6dSv69++P1atXIzIyEmVlZUzHbBKhoaEwMDBA9+7dmY5CNSP19fWYNm0afHx8EBUVhZEjRzIdiREiIiIYPnw4jh8/jsLCQly4cAE1NTWwt7eHgoICFi1ahHv37gnNCtgfYWRkhIcPH2LDhg1YtWoVDA0Nce/ePb7UsrGxQXR0NC5evAgnJyfU19fzpc730tTUhIqKikBXCwcFBcHc3BxSUlICq0lRFPW9aFOYoiihJ4gREtHR0ZCSksKQIUP4VuNrAgMDMWbMGLi5ucHT0xMiIvx5aa6qqsKGDRvQr18/FBQU4P79+zh+/Di6devGl3o/Ky0tDatWrYKSkhJGjRqF9+/f4+TJkygsLMSpU6dgYmLCt7+jlsLa2hpRUVG4dOkSnJycUFdXx3QkqplwcHCAv78/0zH4pr6+vnHneS6XizFjxuDGjRtISkqCvLw8Nm3ahGHDhjGcsmmEhobS0RHUD6mtrcW4ceNw/fp1xMXFtZifhV8lISEBR0dH+Pv7o7CwEFu3bkVKSgoMDQ2hrq6OzZs346+//mI65g9p06YNlixZghcvXkBFRQW//fYb5s6diw8fPjR5LSMjI8TFxeHGjRuwt7dHVVVVk9f4XiwWS6AjJBoaGhAcHAw2my2QehRFUT+KbjRHUZTQe/36Nfr06YNHjx5h4MCBfKkxY8YMVFRU4Nq1a3w5/tdcunQJU6dOxaZNm7B27Vq+1CCEwM/PD8uWLQOXy8Xu3bsxefLkv82jZVphYSEuXbqE8+fP4/Hjxxg0aBCmTp2KSZMmQVZWlul4zdaTJ09gaWkJPT09+Pn5QVJSkulIlJB78eIF+vXrh/T0dPTr14/pOE3u5cuXcHJywt27d5mOwlc5OTlQUlLi6/sm1bJUVlbCwcEBz549Q0xMTIv8+W9q2dnZ8PHxwfnz5/HixQsMGTIETk5OGD9+PLp27cp0vB8SHR2N+fPno7S0FLt378a0adOa/Ab8y5cvMXLkSPTs2RMhISGQkZFp0uN/r4SEBJiamqKwsJDvCyOSk5NhZGSE/Px8yMnJ8bUWRVHUf6MbzVEU1SIoKys3NrX4gcfjITIyEtbW1nw5/tecOnUKTk5O8PDw4FtDODU1FWZmZpgyZQqmTJmCjIwMTJkyRSgawlVVVbh06RJsbGygqKiIvXv3wsrKCs+ePcPDhw+xePFi2hD+RTo6OkhKSkJ6ejqsrKxazGPxFP9oaGhAQ0OjxY6QUFZWblwJzeVyGU7DP2FhYZCXl4euri7TUahmoKysDJaWlnj16hWSkpJoQ/g79e7dG2vWrEF6ejoePnyI3377DZs3b0aPHj0wZswY+Pr6orq6mumY32XkyJFITU2Fm5sb5s6dixEjRiA1NbVJa6iqqiIpKQklJSUwNTXl60Z33zJ06FB07twZoaGhfK8VFBQEQ0ND2hCmKEpo0aYwRVHNAofDwdWrV/kyQuLJkycoKioS6DzhAwcOwNXVFUeOHMHixYub/PgfPnzAggULoKurCykpKTx79gzbt2+HtLR0k9f6ETweDzdv3oSzszPk5OTg6uqK7t27IyoqCtnZ2di+fTs0NTUZzdjSCMtFGNV8ODg4tNimsJiYGERERHDo0CEcOnQIKSkpjeMkACAvLw87d+5kMGHT+DI6QhhuAFLCrbi4GKampigtLUViYiKUlZWZjtTssFgsDBo0CHv37kVubi7CwsIgIyMDZ2dnyMrKwtnZGbGxsUI/419cXBzr1q1Deno6ZGRkMHDgQCxbtgwVFRVNVkNRUREJCQlo06YNjIyMkJ2d3WTH/l5iYmKws7NDYGAg32sFBQXR0REURQk12hSmKKpZ4HA4ePnyJdLS0pr82BEREdDS0oKiomKTH/uf7NixA0uXLsW5c+fg6urapMduaGjA0aNHoaamhqioKISEhCAkJASqqqpNWudHPX/+HGvWrIGysjIsLCyQn5+Po0ePorCwEGfPnoWZmRlfN9dr7b5chImJicHY2Bhv3rxhOhIlxBwdHfHgwQPk5OQwHaXJ5eXlYeLEiTh79izu3bsHa2trzJ8/H+/fvwcAFBQUwMfHh+GUv6aqqgqxsbGwtbVlOgol5PLy8jB8+HCIiIggISEBCgoKTEdq9sTExGBpaQlvb28UFRU1ftaxtLSEkpISVq5ciadPnzId85uUlZUREhICf39/XLt2DRoaGvD19W2yhRldu3bFjRs30KtXLxgZGSE9Pb1Jjvsj2Gw2oqOj+Trf+MWLF8jIyKBNYYqihBptClMU1Sz07dsXurq6uHr1apMfOzIyUiCrhAkhWLduHdavXw9fX184OTk16fETExMxePBgrFixAqtWrUJaWhpsbGyatMaPePfuHQ4cOAB9fX1oamoiPDwcbm5uyM3NRWRkJCZPnoz27dszlq+1+XIRpqCgACMjI7x48YLpSJSQ0tPTQ8+ePQWyikrQsrKy8PLlSzx48AA+Pj7IyMhAdXU1hg4diszMTBBCmv1GlrGxsSCEwNzcnOkolBDLysqCsbExunTpgtjY2GY3A7c5aN++PSZPnozw8HDk5eVh5cqViIuLg46ODrS1tbFr1y7k5uYyHfOrxowZg/T0dMyYMQNOTk6wsrJCZmZmkxxbWloaYWFh0NfXx/Dhw3H//v0mOe73GjlyJAghiI6O5luNoKAgqKmpQUNDg281KIqiflXz/tRLUVSrwuFwmnyucFlZGZKTk/k+T5gQgqVLl2LPnj0IDAyEo6Njkx07NzcXkydPxogRI6CtrY3MzEysWLECbdu2bbIa36umpga+vr4YPXo05OXlsWPHDpiamuLp06dISUnB0qVL0aNHD4Hnoj7r0KEDIiIiMGjQIBgbG+Phw4dMR6KEEIvFarEjJMTFxSEjI4Pc3FzweDx06NABZ8+exdq1a+Ho6IijR48yPmbnV4WGhsLMzIzedKO+Kj09HUZGRlBVVcX169fRsWNHpiO1eLKysli0aBHu3buHFy9ewN7eHkePHkWvXr1gZmaG06dPo7y8nOmY/0NSUhLbtm3D06dP8X/s3XlcjdkfwPHPva1Sssu+rzP2DIPsWbJUFEVkkL0Y+z5iDMPYsmTGGmXfoixZImRs2fcGkcoSKUvbvef3Rz93psmSlFvmvF8vL7rPec7zfa66Pc/3Oed7VCoV1atXZ/LkyZkywtbQ0JAtW7bQsWNHWrRoweHDhzMh4vTJnTs3lpaW+Pr6Ztkx3paOkGV8JEnK1oQQH/2T0kySJEm7bty4IQBx9erVTOtz69atInfu3CI+Pj7T+vy35ORk4eLiInLnzi0OHz6caf2+efNGzJgxQxgZGYm6deuK4ODgTOv7U6hUKnH06FHRr18/kSdPHmFkZCR69uwpAgICRHJyslZikj4sKSlJ9OzZU5iYmIjAwEBthyNlQ4GBgUKpVIonT55oO5RM9erVKzFhwgTRqlUr8fTpUyGEEGq1WgghxKlTp4SxsbFo1KiRNkP8LGq1WpQoUUIsXrxY26FI2dS5c+dEgQIFhK2tbZZe+0gfp1arxYkTJ8SgQYNE/vz5hYGBgbC3txe+vr4iISFB2+GloVarxYYNG4SZmZkoU6aM2L17d6b0q1KpxI8//ij09fXFjh07MqXP9FixYoUoVKhQllyrRkVFCYVCIY4fP57pfUuSJKXX/3O5H873fqyBkElhSZKykW+//Va4u7tnWn99+/YVHTt2zLT+/i0pKUn06NFDmJqaZlrSVq1WC19fX1GuXDlRqFAhsWLFCqFSqTKl709x8+ZNMWnSJFG6dGmhUChEq1atxNq1a0VcXNwXj0X6dCqVSri6ugoDAwOxa9cubYcjZTNJSUmiQIECYuXKldoOJUvcuHFDhIeHp3k9MjIyRz8ouXDhggDE3bt3tR2KlA0dO3ZM5MmTRzg5OYmkpCRthyP9Q0JCgvD19RX29vbCwMBA5M+fXwwaNEicOHFC8+Aqu4iJiRHDhg0TSqVSWFtbi3v37n12n2q1Wvz8889CR0dHrF69+vODTIe3idtjx45let/Lly/PsoSzJElSeqUnKSzLR0iSlKNkZgkJIQT79u3LstIRiYmJdOvWjf379xMYGMj333//2X3evHkTKysrOnfuTMeOHbl16xZ9+/b9YjUwnz59ypIlS6hfvz6VK1dm586dDBkyhAcPHnDgwAF69uyJsbHxF4lF+jxKpZKFCxcyfvx4bG1t8fb21nZIUjaiq6uLtbX1V1lCIj4+Hn19feLj41GpVCQlJXH9+nXOnj1LgQIFaNasmbZDzDA/Pz++/fZbypQpo+1QpGwmICCA1q1b06NHD7y8vNDV1dV2SNI/6Ovr06lTJzZv3syjR4+YM2cON27coHHjxlSoUIEpU6Zw8+ZNbYcJgKmpKQsWLODcuXM8fvyYqlWrMnPmTBITEzPcp0KhYOLEiSxatIi+ffsyf/78TIz43YoUKULDhg2zpH6+r68vnTp1kosoS5KU/X0sayzkSGFJkrKRK1euCEDcuHHjs/u6dOmSAMSdO3cyIbLUXr9+Ldq1ayeKFi2aKeUuXrx4IUaNGiV0dXVFy5YtxZUrVzIhyvSJj48XW7duFZ06dRK6urqiSJEiYsSIEeL8+fPZbvSKlDELFy4USqVSeHh4aDsUKRvZvXu30NfXF7GxsdoOJdO8efNGTJ8+Xejq6opSpUqJ/v37ixkzZog8efKIihUrioYNG4r79+9rO8wMa9CggRg7dqy2w5Cyme3btwt9fX0xZswY+Xs7h7l//7749ddfRfXq1QUg6tWrJxYuXCiioqK0HZoQImXW0YoVK0SBAgVElSpVxKFDhz67z/Xr1wtdXV0xadKkLP9+nT17tqhQoUKmHicuLk7OwpIkKVtAjhSWJOlrU61aNapUqcK2bds+u699+/ZRuXJlypYtmwmR/S0uLg4rKyuuXr1KUFAQ1apVy3BfarUaLy8vKlWqxNatW9m0aRMHDhzgm2++ycSI0xJCcOLECQYMGICZmZlmBPDu3bsJDw9n7ty51KpVSy6e8ZVwc3NjzZo1/Pjjj0ybNu3tA2HpP65Vq1bo6+uzd+9ebYeSac6cOcPZs2d59OgRYWFhPHv2jM2bN/PixQtu3bpF165dGTdunLbDzJAnT55w6tQpOnTooO1QpGzE29sbe3t7fvrpJ2bNmiV/b+cwJUuWZMyYMVy6dImLFy/SrFkzZs+eTfHixbGysmL9+vW8evVKa/EplUr69u3LzZs3sbCwoFWrVnTv3p3IyMgM9+no6Iivry+//fYbQ4cORa1WZ2LEqVlbWxMaGsr169czrc+AgAB0dHRo1apVpvUpSZKUVWRSWJKkHEWhUGRaCYm9e/fStm3bTIjqbzExMbRu3Zrw8HCCgoKoUKFChvs6c+YMDRs2ZNCgQQwZMoRr167RuXPnLL2hCw0NZerUqVSoUAELCwtCQ0OZP38+UVFR+Pj40LZtWznl9CvVs2dPtm/fzi+//MKPP/6YpTdhUs5gaGiIlZXVV1VCQqFQYGJiwuvXrwEYOXIkTk5Omu0FChTg2bNn2grvs+zdu5d8+fLRoEEDbYciZROenp44Ozszf/58JkyYIBPCOVyNGjWYPXs2YWFhBAQEYGZmxsCBAylSpAi9evUiICCA5ORkrcRWoEAB/vjjD4KDg7lx4waVK1dm4cKFGY7HysqKAwcO4OPjg5OTE0lJSZkccYpKlSpRpUoVfH19M61PX19fWrduTa5cuTKtT0mSpKwik8KSJOU4dnZ2nD9/nr/++ivDfcTFxXH8+PFMrSf85MkTmjdvTmxsLEFBQZQuXTpD/Tx69Ig+ffpQv359SpcuzY0bN5g8eXKWXVw+e/aMZcuW0bBhQypWrMiWLVtwcXEhLCyMQ4cO0bt3b/LkyZMlx5ayl06dOrF3715WrlxJnz59tHZzKWUftra2+Pv7k5CQoO1QMkWdOnUoVaoUbm5uPH36lAYNGjBq1CggJan6+++/07FjRy1HmTF+fn7ywZ2kMXv2bIYOHcqKFStwdXXVdjhSJtLR0aFFixasWrWKR48esWrVKmJiYmjfvj0lS5ZkxIgRhISEaGXWT4MGDTh9+jQzZsxgypQpmJubc/LkyQz11bhxY44cOcKhQ4ewsbHRPMzLbNbW1pmWFE5OTsbPzw8bG5tM6U+SJCmrKdLzy0KhUAg5lVSSpOxCCEGlSpVwcXFhzJgxGerD19cXBwcHnj17linJ1oiICCwtLTEwMCAgIICCBQt+ch9JSUksWrQId3d3SpcujYeHR5YteJSYmMiePXtYu3Yt/v7+5M2bF0dHR3r27EmdOnXkaKL/uLNnz9K2bVsaN27Mxo0bMTQ01HZIkpbExsZSqFAhduzYgZWVlbbDyRSJiYkEBweTL18+atasCaSc5+bNm8mVKxc9evTQcoSfLikpiYIFC7Js2TIcHR21HY6kRUIIJk+ezOzZs/Hx8cHe3l7bIUlfyNOnT9m8eTPe3t6cPHmSqlWr4uTkRPfu3bWy+GRUVBSjRo1i/fr19OnTh1mzZmXo+vj27dtYWlpSsmRJdu/eTd68eTM1zpMnT9KwYUMiIiIoWrToZ/V15MgRWrZsyaNHjzJ0rpIkSZlJoVAghPjgjb0cKSxJUo6jUCiwt7f/rBIS+/bto3nz5pmSEA4LC6NJkyaYmppy+PDhDF0EBgQEUKNGDX7++Wd++eUXQkJCMj0hLITgzz//ZPDgwRQtWhRHR0f09fXZvn074eHhLFiwgLp168qEsIS5uTnHjh3j7NmzWFlZERcXp+2QJC3JkycPrVq1+qpKSOjr69OsWTNq1qyJSqUC4P79+xQvXpwePXrkyJrax48f59WrV7Rp00bboUhapFarGTZsGHPnzsXX11cmhP9jChYsyODBgwkODiY0NBQHBwfWrFlD2bJladKkCX/88QfPnz//YvGYmZnh7e3NoUOHCA4OpnLlyixfvvyTy1NVrFiR48ePEx0dTfPmzXn06FGmxlm/fn2KFCnC7t27P7svX19fGjduLBPCkiTlGDIpLElSjmRnZ8eZM2e4d+/eJ+8rhMi0esK3b9/GwsKCUqVKERAQ8MmjF+7cuYONjQ3t2rWjWbNm3Lp1iyFDhmTq9N87d+4wbdo0KleuzPfff8+1a9eYPXs2UVFRbNy4kfbt26Onp5dpx5O+DlWrVuXEiRM8ePCAli1bEh0dre2QJC2xtbXF19dXk0DNycLCwvDy8mLnzp3s27ePvXv3curUKZYtW8avv/7K+fPnuXbtmrbD/GR+fn40bNiQ/PnzazsUSUtUKhV9+/Zl9erV7Nu3L1PLY0k5T/ny5ZkyZQo3b97k9OnT1KpVi0mTJmFmZkbnzp3Zvn078fHxXySW5s2bc+HCBcaMGcPw4cNp2LAh58+f/6Q+SpQoQVBQELq6ulhYWBAWFpZp8SmVSjp16sTOnTs/qx8hBL6+vlhbW2dSZJIkSVlPJoUlScqRateuTdmyZdm2bdsn73vz5k3CwsI++4bp6tWrNGnShOrVq+Pv74+xsXG693316hWTJk2iWrVqREdHc/bsWTw9PTNtZEFMTAzLly/HwsKC8uXLs379epydnbl79y5Hjhyhb9++mJqaZsqxpK9X6dKlOX78OImJiTRp0oSHDx9qOyRJCzp16kR0dDQnTpzQdiifLSwsjLFjx9KnTx9WrFiBn58fGzZsICQkhKioKBYuXMjp06e1HeYn8/f3p0OHDtoOQ9KSxMREHB0d8fX15dChQzRt2lTbIUnZhEKhoF69enh4ePDw4UN27NhBrly5cHJyomjRovTv35+goKAsX1xWX1+fsWPHcv36dYoVK4a5uTlubm68ePEi3X0ULFiQw4cPU7JkSRo1apSpD/Csra05dOjQZ82MunLlCnfv3pVJYUmSchSZFJYkKUdSKBTY2dllqITE3r17KVeuHBUqVMjw8UNCQmjatCmNGjXSXGCnhxCCjRs3UqVKFdasWcPq1asJCgqidu3aGY7lrcTERHbt2oW9vT1mZmZMmDCBWrVqcfr0aa5fv87EiRO1UlNOytmKFCnCkSNHyJcvH40aNeL27dvaDkn6wgoXLkzjxo3Zvn27tkP5bE2aNCE0NBQ7Ozvi4+MZPHgwCxYsoGvXrjRs2JA1a9bQq1cvbYf5SW7fvs3NmzdlUvg/6vXr19jY2BAUFMTRo0f57rvvtB2SlE3p6elhZWWFj48Pjx49wsPDg7CwMJo3b07ZsmWZMGECV69ezdIYSpUqxfbt29m9ezf+/v5UrlwZHx+fdJftMTExwd/fn3r16tGkSRPOnDmTKXG1bNkSPT099u/fn+E+du7cybfffkv58uUzJSZJkqQvQSaFJUnKsezs7Pjzzz958ODBJ+33dlplRmvnBgcH07x5c9q1a8fGjRvR19dP134XL16kadOmODs706tXL27cuIGjo+Nn1fAVQnD69GlcXV0pXry4pn7gli1biIiIYNGiRdSrV0/WCZY+S968eQkICKBq1apYWFhw8eJFbYckfWGdO3dmx44dObLe7j8JITA2NuaPP/7ghx9+oGfPnqxbt46XL1/m2M9Jf39/ypQpQ9WqVbUdivSFxcbG0q5dO65evcqxY8eoXr26tkOScggTExN69uzJ/v37CQ8PZ9iwYezbt49vv/2W2rVrM2/ePCIiIrLs+FZWVly5coUBAwbQp08fWrRowfXr19O1r6GhIVu2bKFjx460aNGCwMDAz47H0NCQNm3a4Ovrm+E+ZOkISZJyIpkUliQpx6pXrx4lS5b8pNFrr1+/5ujRoxmuJ3z48GFat26No6MjXl5e6ar9Gx0dzeDBg6lTpw758uXj2rVrzJgx45PKTfxbWFgYM2bMoGrVqtSvX58LFy4wY8YMoqKiNBfKsk6wlJmMjIzw9fWlWbNmNG3a9KsoJSCln42NDffv3//kOpDZzdvEr1qtpkuXLgQGBnLmzBmmTJlC8eLFtRxdxrwtHZFTk9pSxkRHR9OqVSsiIyM5duwYFStW1HZIUg5VtGhRRowYQUhICFevXsXKyoqFCxdSsmRJLC0t8fLyypIFZ3PlyoW7uztXrlzBwMCAGjVqMG7cOF69evXRfXV1dVm5ciUuLi60a9fus+sBQ0oJCX9/f5KSkj553/DwcM6dOyeTwpIk5TgyKSxJUo6VkRISR44cQQhB8+bNP/l4/v7+WFlZ0b9/fzw9PVEqP/wRmpyczNKlS6lYsSKHDx/G398fX1/fDE8re/HiBStXrqRZs2aUKVOGNWvW0L17d/766y+OHTtG//79yZcvX4b6lqT00NfXx8fHB0dHRywtLdm3b5+2Q5K+kNKlS1OnTp2vooQEpCwsJIQgf/78eHh4EBwcrLmZ19HR0XJ06RcbG8vRo0dl6Yj/mMjISJo1a0ZCQgLHjh2jVKlS2g5J+kpUq1aNGTNmaNagKFeuHMOHD6dIkSI4OjpmOGn6IRUrVmTv3r1s2LABb29vqlatmq6ZKUqlkrlz5zJ58mTs7Ozw8vL6rDjat29PbGwsx48f/+R9d+3aRbFixahbt+5nxSBJkvSlyaSwJEk5mp2dHSdOnEj3FLe9e/fSpEkTcufO/UnH2bZtG7a2towZM4a5c+d+dETW0aNHqVu3LuPGjWPixIlcunQpQ6OTk5KS8Pf3x8HBATMzM8aMGUO1atU4efIkt27dYsqUKZQrV+6T+5WkjNLR0WHp0qUMHz6cTp06sWnTJm2HJH0hb0tIfC3++TneoEGDHHkzf+DAAfT09OTCYv8hYWFhmuuYwMBAihQpou2QpK+QUqnEwsKC33//naioKHx8fEhMTKRz584UL14cNzc3Tp8+nWklhd4O9Lh+/TrdunXD3t6eDkj5fKEAACAASURBVB06cOfOnY/uN3HiRDw8POjTpw8LFizIcAwFChTAwsIiQ6OOd+7cibW19UcHjEiSJGU38lNLkqQcrUGDBhQtWjTdiYq39YQ/xbp16+jWrRvTp09n2rRpH0wIP3jwAAcHB5o3b06dOnW4desWI0eOTHfdYUipeXnu3DmGDx9OiRIlsLW1JTExkQ0bNhAZGcnSpUtp0KCBnCosaY1CoeCXX35hxowZdO/enT/++EPbIUlfgK2tLdeuXePmzZvaDiXDYmNjiYiI4MmTJzx9+pRnz54RGxvL69evSUxMRKVSaTvET+Lv74+lpSWGhobaDkX6Am7duoWFhQUlS5bkwIED5M+fX9shSf8BBgYG2Nrasm3bNqKiopgxYwYXL16kfv36VK5cGXd3d0JDQzPlWCYmJsyZM4cLFy4QFxdHtWrVmDZtGvHx8R/cb/DgwXh7ezN69GgmT56c4WS1tbU1vr6+n7T/ixcvOHLkiCwdIUlSziSE+OiflGaSJEnZk6urq2jWrNlH292+fVsA4urVq+nue9myZUKpVIpFixZ9sN2bN2/E9OnTRa5cuUS9evXEn3/+me5jvHX//n0xc+ZMUa1aNQGI77//Xnh6eoro6OhP7kuSvpTly5cLpVIpZs2ape1QpCymVqtFpUqVxMyZM7UdSoYtWrRIKBQKoVAohIGBgdDT0xO6urpCR0dHGBgYCIVCIWbMmKHtMNNFpVKJwoULi+XLl2s7FOkLuHDhgihcuLBo3769eP36tbbDkSRx79498csvv4iqVasKQDRo0EAsXrxYPHnyJFP6V6vVwsvLSxQqVEhUqFBB7Nu376P7+Pn5CUNDQzFkyBChUqk++Zh37twRgLhw4UK699mwYYMwMTER8fHxn3w8SZKkrPT/XO6H870fayBkUliSpGzu6NGjQqlUiqioqA+2W7RokShVqpRQq9Xp6nfevHlCqVSKlStXvreNWq0WO3bsEGXLlhWFCxcWq1at+qSL0NjYWLF69WrRokULoVAoRLly5cRPP/0kbt++ne4+JEnbtmzZIvT09MSYMWPS/fMl5Uzjxo0T3333nbbDyJC335seHh7ixx9/FImJiVqO6POcOnVKAOLhw4faDkXKYidPnhR58+YV3bp1y/Hft9LXR61Wi5CQEDFixAhhZmYmdHV1RYcOHcTGjRsz5QHGs2fPxODBg4VCoRB2dnbiwYMHH2wfFBQk8uTJI7p3756hn5fq1asLd3f3dLd3cHAQXbt2/eTjSJIkZbX0JIVl+QhJknK8Ro0aUahQoY/WANu7dy9t27b9aNkFIQTTp09nzJgx+Pj40KdPn3e2u379Om3atMHe3h5bW1tu3brFDz/8kK4F6Pbt20ePHj0oUqQIP/74IxUrVuTYsWOEhoYydepUKlSo8OGTlqRsxM7ODj8/PxYvXsyAAQNy3BR8Kf1sbW05ffo04eHh2g7lkykUCoQQuLq6Uq9ePY4ePQqAWq3WcmQZ4+/vT506dShWrJi2Q5Gy0OHDh2nVqhVdunTBx8cHPT09bYckSakoFApq167N3LlzCQ8PZ8+ePeTPn5++fftSpEgRfvjhBw4dOpTha4N8+fKxZMkSTp8+TVhYGFWqVOG3335774J3FhYWHDlyhIMHD2Jra8vr168/6XhvS0ikR2JiInv27MHGxuaTjiFJkpRdyKSwJEk5no6ODl26dGHr1q3vbRMfH09gYOBH6wkLIRg/fjw///wzW7duxcHBIU2bFy9eMGLECGrUqAHApUuXmDt3Lqamph/s98KFC4wcOZKSJUvSsWNHXr16xbp164iMjGTZsmU0atRI1gmWcqzWrVtz8OBBtmzZgqOjI4mJidoOScoC5ubmlChRIkML8WQHbz9jHR0dadWqFZCyoJJKpcpxyWE/Pz86dOig7TCkLOTn54eVlRUuLi4sX74cHR0dbYckSR+ko6ODpaUlXl5ePHr0iN9//53Hjx/Tpk0bSpUqxejRo7l48WKGav6am5tz8uRJfvvtN2bMmEHt2rU5duzYO9u+3Xb58mXatm3Lixcv0n0ca2trQkJCePDgwUfbHjlyhNevX3/yeiWSJEnZhUwKS5L0VbCzsyMwMJCnT5++c3tQUBBJSUm0aNHivX2o1Wrc3Nzw8PBg9+7daRaMUKvVrFq1ikqVKrFz5062bt3K/v37qVq16nv7fPjwIXPmzKFGjRrUrl2bEydOMGnSJCIjI9m5cyddunSRCwRJX43vv/+eo0ePEhQURKdOnXj16pW2Q5IymVKpxMbGJt2Le2ZHFy9exNPTk927d5OYmMjp06cZOXIkI0aM4PLly9oOL10iIiIICQmhffv22g5FyiKbNm3C1taWcePGMW/ePPnQWMpxcufOjaOjI/7+/kRERDBu3DiCgoKoVasWNWrU4Ndff01X4vWfdHR0GDhwIDdv3qRevXo0bdoUZ2dnHj16lKZtpUqVOHHiBE+ePKFZs2bvbPMudevWpXjx4uzateujbX19fWnWrBl58+b9pPOQJEnKLmRSWJKkr4KFhQX58+d/7+i1ffv20bhxY/LkyfPO7SqVChcXF7y8vNi/fz+tW7dOtf3PP/+kQYMGuLq64ubmxrVr17C2tn7nTdrLly9Zt24drVu3pmTJkixduhQbGxtu3LjBn3/+yZAhQyhYsODnn7QkZUM1atTg+PHj3Lx5E0tLS54/f67tkKRMZmtry9GjR4mOjtZ2KJ/Mz8+PgQMHEhAQwLRp0+jQoQOrV6+mXLlyxMTE4OTkRHBwsLbD/Kg9e/ZQuHBhzM3NtR2KlAVWrFhB9+7dmTVrFlOnTpUJYSnHK1y4MK6urpw6dYqbN2/SuXNn/vjjD0qXLk3z5s1ZuXIlMTExn9Tf6tWrCQoKIiQkhMqVK7N06dI0JSpKlCjBsWPH0NXVxcLCgrCwsI/2rVAosLa2/uiMGCEEu3btSjOIRJIkKSeRSWFJkr4Kurq62NravreExNt6wu+SlJSEk5MT27dv5+DBg1hYWGi2RUVF0bt3bxo2bEj58uW5ceMGEydOTDO6V6VSceDAAXr27ImZmRlDhw6ldOnSHDlyhL/++ovp06dTuXLlzDthScrGKlSowPHjx4mNjaVp06ZERUVpOyQpEzVp0gRTU1N2796t7VA+2Zo1a3B0dGTHjh2cOXOGPHny0LhxY9zc3PD09MTIyIhLly5pO8yP8vPzo3379h+tYS/lPAsWLGDAgAF4enoycuRIbYcjSZmuUqVKuLu7ExoayokTJ6hWrRpjx47FzMwMe3t7fH19012CqnHjxoSEhDBlyhTGjh1L/fr1OXPmTKo2BQsW5PDhw5QoUYJGjRpx/fr1j/ZrbW3NkSNHPpioDgkJITw8XCaFJUnK0eSVpCRJXw07OzuCrtxj8vYQOnueoIb7fqpM2cu3P+3luXlfosy+5/LD1DXF4uPjsbOz49ChQxw5coTvvvsOSFk44rfffqNSpUqcP3+eI0eOsGHDBkqWLJlq/8uXLzN69GhKlSpFu3btiImJYdWqVURFRbF8+XKaNGkib9ql/6TixYtz9OhRcuXKRePGjbl79662Q5Iyia6uLp06dcqRJSRMTU01ixO9fPlSU08YUh7uKRSKbF+3NT4+ngMHDsjSEV8ZIQTTpk1j9OjReHt7079/f22HJElZSqFQ8P3337NkyRIiIiLYsmULSqUSBwcHihYtyqBBgzhx4sRH6w/r6ekxYsQIbty4Qfny5alfvz6DBg1KNVPJxMSEPXv2YG5ujoWFBWfPnv1gn82aNcPIyIi9e/e+t83OnTupU6dOmnsDSZKknESRniLvCoVCZKQYvCRJ0pdy5t4zJu+8zI1HL9FBoCLtVEs9HQVJKkG1onmY1ukbqhU2xMbGhqtXr3Lo0CGqVKkCpJSaGDZsGE+fPuXnn3/GxcUFXV1dTT+RkZGsX7+edevWcfHiRczNzenZsycODg4ULlz4i52zJOUEL1++xMbGhuvXrxMQEMA333yj7ZCkTLBr1y66du3K06dPMTY21nY46Xb69Gl+/fVXChQowJ07d3jz5g2lS5emZs2aXLhwgXv37rFo0aJsXZZh//79dOzYkadPn763JJKUswghGD16NIsWLWLLli106tRJ2yFJkta8ePGC7du34+3tTWBgIGXKlKFHjx706NFDc63+IQEBAQwdOpSYmBhmz56Ns7OzpgRLcnIyLi4ubN26lV27dtG8efP39vN2semNGze+c3uNGjWws7NjypQpGThLSZKkrKdQKBBCfLAGlUwKS5KUoyWr1Mzcd4OVx++iVIA6HR9Vb9uZPDxN3HFvDh88QLly5QgNDWXEiBH4+/szcOBApk2bRoECBQB49eoVO3fuZN26dRw4cIDixYvj5OREz549P7jQnCRJkJCQgKOjI0ePHmXv3r2aEflSzqVWqzl79izly5fXfE7mFCqVipMnT2JsbEytWrW4dOkS69evR6lU0qtXr3QlHbRp5cqVhIeH89NPP2k7FCkTqFQqBg8ejLe3N76+vrRq1UrbIUlSthEeHs6GDRvw9vbm0qVLmJub4+TkhIODA0WKFHnvfgkJCcyZM4cZM2Zgbm7O0qVLqV69OpDy+2vkyJF4enqyadOm95Z/2LBhAwMHDuTJkyfo6+un2nb37l3KlSvHhQsXqFmzZuadsCRJUiaSSWFJknI8lUpFTEzMO5MOySo1g9eHcPjGY5LTkw3+N7UKi/L5WORQi9mzZjJ37lwaNGiAh4cHNWvWRK1W4+fnx9atWzXTpO3t7enZsydNmzaVZSEk6RP8c3SOr68vLVq00HZI0n/Us2fPePLkCXnz5qVIkSK8ePGC27dvU6hQIUqXLq3t8D5ICEFycjJKpTLbl7mQPi4pKQlnZ2f27NnDnj17aNiwobZDkqRs69KlS/j4+ODj40NUVBSWlpY4OTlhY2ND7ty537nP3bt3cXV11cwCnDp1KiYmJgghmDFjBlOnTmXlypU4Ozun2TcmJoZi3zbAacJ8niryEPr4JYkqNfo6SnKr4oi8dIJdi3+iRom8WX3qkiRJGSKTwpIk5XgeHh5MmTKF0NBQChYsmGrbdP9reAXfe2dCOOHhdaLWjaZov6XoFyz13v6VCB4s7oWpgZIlS5bQtWtXrl27xrp16/Dx8SE8PJyKFSvSoEED7t+/z969e8mVK1emn6ck/Reo1WpGjRrFkiVL2LRpEzY2NtoOSfqPuXfvHr179+bcuXMULVqUkSNHsmnTJu7fv090dDTDhw9n/PjxaUaFSVJmi4+Pp1u3bpw8eZKAgABq1aql7ZAkKUdQqVQEBQXh7e3N1q1bUalU2Nra4uTkRMuWLVOVfIOUh2m7du3Czc2N5ORk5s+fj729PQqFgiVLluDm5sa8efMYNmyYZp8z954xZdcVrkfGoRAqhCLtQziFUCMUSk1ZOvMy+bP83CVJkj5FepLCcpibJEnZ1uXLl5k8eTL16tVj4MCBJCcna7adufeMVcfvkqwWqBPfIJITUy9EoaMHgNLAKFWfIjkJkfz3isZqFChz5+fHqXOIjIzE3Nycb7/9loCAAEaMGEGXLl0YMmQIq1atonz58ty4cSNrT1qSvmJKpZK5c+cyZcoU7Ozs8PLy0nZI0n/M/PnzadOmDXFxcezevZuxY8fSoUMHQkNDiY6OJigoiKNHj2o7TOkr9/LlSzp06MC5c+cICgqSCWFJ+gQ6Ojo0b96clStXEhUVxerVq4mNjaVDhw6UKFGCH3/8kXPnzmnuCxQKBdbW1ly7dg1nZ2ecnJxo06YNt27dYsiQIaxbt45Ro0YxZcoUkpJVTPe/hv3vJ7kZFQfwzoRwyuspqZQbUbHY/X6S6f7XSFapv8ybIEmSlEnkSGFJkrKlsLAwmjZtytChQxk+fDhdu3YlMTGRDRs2YGJiQjuPIG5GxaEW8HT3b7y6eiTdfedr6UKeen/XD4tcMxzjWu3IdXk7+vr63L179737fvPNN1y5cuVzTk2SJHjv6BxJykqjR4+mdOnS9O7dG2NjY2bOnImVlZWmJqSFhQXjxo2jffv2Wo5U+lrFxMRgZWXFo0ePOHToEGXKlNF2SJL0VXj69ClbtmzB29ub4OBgqlSpgpOTE927d6ds2bKadjdu3GDIkCEcP36cMWPGMGHCBA4fPoydfVdqDPYg2qDoJ5elE6pkdJXQ8pviLO1eB12dzx97J4QgPj5ezlCUJCnDZPkISZJypMuXL9OhQwf69evH5MmTgZS6ewMHDuTo0aOM+Gkms6/9PQJYnfAalEoUugaa1YUTH90hcrUbJYauQ8c4399tk+IBUOoZkhAVyqtLB3l18ziGJaoxvFdnrp4+ho2NDb179wZg3LhxAMycOZOkpCTUajWGhoZf4m2QpK+ej48PvXv3Zvz48bi7u2t+fqXsadeuXQwfPhyA5cuX07Jlyw+2X7BgAf7+/pQpU4aZM2dqSgCNHj2aVatWoa+vz4IFC+jWrZtmn6tXrzJ16lQ2bNigmQIshGDy5MmEhITQsmVLRo4cCcD9+/dxc3Pjxo0b1K9fn8WLF2NiYgKkfGavWLGCb775hl27dmn6Dw0NpX///nTp0oUBAwZojqFWq5k0aRKnTp1i2bJlVKxYMc35qFQqAE0t3+nTp3Ps2DGKFCnC7NmzKVq0KDt27GD58uWo1WqUSiVGRkaUL1+emTNnolQqEUKgUCgYMmQICxcu1Bzf09OTwMBAmjVrxuDBgzXH/P333zl16hSFChVi/Pjx5M2bUrsyIiKCefPmoVKpGD9+PIULFwZSymOsXr2aly9fYmlpSdu2bT/+Hyt9MY8fP6ZNmzYkJiZy4MABihUrpu2QJCnHio+P58WLF+jp6aVZ5+Pu3bts2bKFzZs389dff9GgQQPs7e1p3749pUqVwtDQkI0bN9KnTx8SExMpWLAgr1UK3iSpgZRrEXXCK3JXa0KBNkM+GktCVChRa36kmNMsBjl0YFL7au9sFxkZSbFixdJVv16tVqOvr09oaOjH3wxJkqR3SE9SWPdDGyVJkr4ktVrN0qVLGTt2LMOHD2fo0KHExMRots+ePZuZM2cypFdXDIpVwrhmW4yqNEZpYIQ6/iUodTRlI97Zf1I8qJJRGhoDoDQ0Rt+sAm/+OoNBgRJEirxpLioLFCjA7du3USgUTJgwAWdnZ83qxZIkfZ4ePXpgamqKvb09z58/Z+HChXIBx2zq8ePH9OrViz179lC1alVatGhBUFCQJgn7b/PmzePUqVPMnz8fLy8vBg0axObNm/njjz+4fPkyx48f5/bt2/Tq1YuWLVtSsGBBrl69SqdOnShUqFCqBwQODg5UrlyZCRMm8NNPP5EnTx5cXFzo3r07PXv2ZM6cOXh6euLm5sbq1atZt24dAQEBeHp68vz581RxVahQgXXr1nHx4kVevXqFqakpALdv3yZXrlzvTQi/dfLkSRo3bszChQu5fv06gwYN4ubNm/Ts2ZM9e/ZQo0YNfvjhB1QqFUqlkjVr1hAZGanZX6FQsHTpUjw9PZk/fz4AGzduZO3atTg6OnLy5EkqV65My5YtWbt2Lfv27cPCwoIHDx4wcOBA1q1bh1KppE+fPpQoUYJixYoxbtw4VqxYgUqlwtXVlYoVK2JkZMRvv/1G7ty5sbCwyPD/u5R5wsPDadWqFcbGxhw9ejTNOgmSJH0af39/HB0dMTAw+ODCm8bGxoSEhHDq1ClGjhxJnTp1mDBhAra2tvj6+vLs2TOO3YigSPdZ8I/fPTHHfFLuL9JBaZAbECjyFGHl8bu0/cbsnTWG38YaHByMn5/fB2NPTEykQoUK6Tq+JElSRsmksCRJ2cLjx49p2rQp0dHRTJ48mSlTpuDh4QGk1N57e+OekJBAbadx/HXrJjEnNmJYtjZKAyOi1o8n6XHasg/hi3um+tqwdE2KOM4AQC+vGXp5zYgL8UO/dC2e56uMUO+mb9++9OvXj9y5c7No0SJOnjxJUlISHh4edOjQIYvfCUn6b+nQoQP79++nY8eOPH/+nNWrV6On9/6HO5J27N69m1atWlG/fn10dHRo0aIFwcHBtGnT5p3t1Wo1CxcuxMzMDBcXFzp27IgQguLFi/PHH39QqlQpqlatioGBAeHh4RQsWJCff/6ZYcOGsWPHDk0/iYmJlClThtGjR2NiYkLnzp05deoU5ubmvHjxgp49e2JkZISrqyu1a9dmxYoVGBgYsHDhQmrUqPHOuIoXL07x4sU1XyuVSkJCQnBxccHMzOy974FSqeTy5cs0atSIOXPm4OfnR61atRBCaJLENWvWpHz58pp9PD09mTp1quZhx/3791m+fDn58uXTvDZjxgxGjRqFs7MzFy9eZPr06bRs2ZJp06axevVqLCwsSE5OxszMjNDQUCIiIggNDcXX1xcDAwPatGnDjRs3uH37NrGxscybNw+An376iT/++INGjRrJhy1a9tdff9GyZUtKliyJn5+f5ppGkqSM69KlC4mJiR9v+H9JSUkcPHgQb29vevbsib6+PoULFyYiIgKVQp8H/7pnEInxGNds/UkxKQ2MUChgyq6r7HFL+0Du7WexWq0mKSlJ89quXbt4/Pgx/fr107RNSEjQtJEkScoqMiksSVK2ULhwYdzd3WnVqhX58+fXlG24d+8eFSpUSDViuIb7fvKVaEze5n00o8kK2YxDoWeAQs8QBZD45B6PfMZh5jwPvXwp0zPV8a8Q4v0LQJy9FU64r6/m69jYWJydnQE0K9E3b94cpVKJQqFI9eddr33q69mlD9m3/D/70n03atSIQ4cOYWVlRefOndm8ebOsoZfNhIWFUbt2bc2IprJlyxIaGvrepPCIESNQKpWoVCoWLFhAp06dUCqVtG/fHoUi5XP7zp07JCcnU6lSJQCWLVvG1atX2bFjh6aNvr4+7u7uGBoa8ubNG7Zt28bAgQOJj48nPj4eI6OUUkLPnz/XfM/Y2Nigr6+PEILo6OhUIzIDAwOJjIwkb9686OjooFKpMDMzY+LEiQwfPpzatWtTq1atd46AFkKgUqlQKBRERERQtWpVIGX0b0JCQqqbfQBvb29KlChBuXLlNH24uLgwYsQIpk6dqjnH27dvY2dnh1qtpmbNmly7dk3z/jRq1AgAXV1dlEoliYmJnDhxgi5dumBgYABA69atOXfuHNevX6dx48aaY1WoUIFz586l579XykJXr17F0tKS6tWrs2PHDs33rCRJX5aenh7t2rWjXbt2xMXFsXPnTsaOHUuCnjHFB65M0/59I4XV8a9SytbpGWo+x1GnLIat0DNALeBaZCznw6Ipn0+PhIQEChUqlKqPEiVKULp0ac39TkREBDo6OixYsEDTplmzZgwZ8vHSFZIkSZ9DJoUlSco2unbtysuXL3n06BH6+vooFApiY2MBNElhIQSvY5+hFrooDf6+sdLLXzxVX0p9o///nStVuYgPyWVsQs2aNenevTtVqlShV69eeHt7Y21tjaurK2fPnmXatGmo1WqEEGn+ZMbr2aWPrI4vp8adXeL7mvn5+WFkZPTORHJ2SWbnlGR7RttOmDAhzQ1scnKypm4tQO7cuVOVRfi3t/Vz69SpQ1hYGBcuXAD4+wYaGDZsGK6urpokmampqSah+s92hoaGXL58mY4dO1KoUCFsbGxQKBTEx8fTp08f6tevj6enJx06dEBHRwcdHR2ESKndu2jRItzd3TV9BQQEMHfuXKpWrUrdunUxNDTE0NCQ2NhYjhw5wsmTJ5k9e/Y7k8LJycmULFkSSHlA2K1bN3r37s3hw4cxMTHRlJ14mxyeN28e69ev1+zv6emJkZERPXr0SFnlPimJpKQkjI2NMTQ01Oz3dmTYd999x4ABA+jevTvBwcEolUoqVqzIq1evKFasmOYcTU1NiYqKonHjxgwYMICGDRtiaGjIwoUL6datW5pRwm/3k7LeuXPnaNOmDU2bNmX9+vWaRL4kSZlj27ZtdO3aVfOZnZiYyPfff8+hQ4fIly8fycnJmoeZL168ICwsjFKlSmFiYkLPnj3x9/dn246dPJjfjX9fXYnkRIyrp62dH713Ia9vBr8znvuz/17Ius6slL8LFCjA06dP07SNj49HqVQye/bsNNv2799PeHh4et4CSZKkzyKTwpIkZSs+Pj4MGzYMA4OURePUajUqlYoy/1+dWwhB3Ot48jSwI2/j7qhev0Cd+Ob/07X+vvFVJ77+/99vUj3lF2oV6oRX6OTKkypJ/Dr0NLpxUcRGRWFpaUnBggUxNjamQ4cOlCtXjm3btjFlyhRatGjxZd4ISfqAdyWKc0IyOz2vR0VFMXr0aHLlysWMGTMwNTXNVvHllL4/5+FLcnJymu85U1NTXrx4ofn6zZs3H6zhCCmJ3T///JNly5bRt29fDh48qElGrly5kgcPHrBp06Z0fc9XrlyZwMBARo8ezcKFCxk9ejT79u1jzZo1HDx4kEuXLrF58+ZUx96+fTu3b99O9XPz66+/0qVLF4YNG0a5cuVwc3Mjb968HD58GHd39w/WjFer1dSvXx9IWQBuwYIF7N+/n99//505c+akWoT03Llz5M6dm2rVUhYbCgsLY/fu3fj7+6NQKNDV1dW0/2fSAlKSwkIIFi9ezNy5c9myZQvLli1j0qRJGBkZaR6avk3uKpVKkpKSaNGiBXZ2dmzcuJEnT55w9epVevTokeY84uPjGTt2LIsXL/4qHmJk176joqLYs2cP5cqVo0iRIowePTpbxJ3T39ec1reUtYyMjKhatSpXrlwBUmac+Pn5abZt27aNBg0aEB8fT65cuTQz/97auHEjnT1PEHI/Jk3f75PfchD5WvVHofj7c/v1X2d4tteDwl3d0S+cMjukRglTljrU0CxS+m86Ojo8f/6cgwcPptl27do18uXL9469JEmSMpdMCkuSlK0MGDCAatWqERYWhpOTU5ryEfPnz2fDlVgewGx0zQAAIABJREFUFzYH4PXNEzzbv/S9/UV5jXjn64U6T0RpkJuYY94kRT9AaWSKSaF8RMbHY2xszM6dOzU1/5o0acLGjRuxt7fX1J+UJG362m82W7RoQdu2bZk8eTIHDhzQjM6UtKdBgwapRjOdO3fugw/J/P39ad++Pbly5aJfv35MnDhRs+38+fOMGzeOgwcPpplKL0TKWK23399xcXHcuXOHmjVrUrZsWXr06MHy5csZPXo033zzDdOmTWPgwIH069dPU4YCIDQ0lEGDBrF3717NawpFyoPG7777jpMnTzJ06FDatm2Lt7c3KpVKc+z3USqVmprDFSpUYPr06WzdupWSJUvi4uKSqu2GDRuwt7fXfL1w4UICAwPp2rUrycnJREZG0rlzZxYvXkzBggV59uwZ+fPnRwihSSCYm5szd+5cdu7cSaFChXB1dQWgUqVKnDx5UvO76OHDh+TNmxdDQ0Pmz59PSEgIDg4OjBo1imLFiqU5D319fYYOHYqNjU22eYiRU/t+38OXhw8fEhwcTJkyZfjmm2949OjRV3fumdX31+5dyePslLTO6X0/ePCAiIgIunXrRoECBbh+/Tr37t1j+PDhxMbGMmfOHExNTTXX9NOnT8fExCRVH5dV9fiUtIiOcdpkbdKjO6DU4c1fZ8lVri4A4Ql6lC5d+r39CCGIiYnhyJEjabY9ffpU8xBSkiQpK8mksCRJ2Y5SqWTQoEFUqlQp1XTlp0+f4u7uTgfXn3kuFCSpBMbVW5G7WrOUesLKv5/YJz66Q+RqN4q5eKJX4O+EklCrEMmJKHR0Ub18jmGp6hSyHY/SwJhwn5HUq1MHf39/pkyZwm+//UZQUBAbN24EICQkBB8fH1xcXGjQoMGXe0Mk6T+mcOHCBAYG0qlTJxo1asTBgwdTJfykL69Jkyb07duXJUuWUKRIEXbv3s2sWbPe2/7XX38lPDycAQMGsHPnTmrWrIlCkTJ6snPnzqxdu5aaNWu+c99/P3hr164dx48fp1y5chw4cIAqVapotiUkJHDo0CFOnz6teS0mJgYHBweWLFlCnTp13tm3ECkjcYODg+nTpw/Xr19/Z8mIf9LVTX3ZnC9fPrZs2YK7uzt58uRJtW3nzp0EBARovnZwcMDCwoKYmBgUCgWnTp2ibdu2GBkZ0blzZ0aPHs3cuXNZvHgxtWvXRqFIGQlcrFgxjhw5wvjx4zUlPSwtLZk4cSIuLi7kz58fLy+vVCOuixYtilKpZPjw4e88Dx0dHSpVqiR/prLItm3bcHR0ZPTo0fzyyy9f9QO8zPCuRHFOSGbnxPhyat8fmvkSFxeHSqUiKCiIokWLIoTgzZs33L17l+TkZC5evEh0dLQmwXrt2jX09fW5ePEiz58/T5lpoW8CahWGZetQsP1wwubYoNDVB1J+dkVyIsY1LCnQZvA7v4fV8S95de0IBdq58ezAMkzqtEevQAkSktOOEH7z5o1mtHJycjLFixdn6NChadoFBQWlmp0jSZKUVWRSWJKkbOPNmzfExcXx7bffMnDgQFavXq0ZGfXy5UvGjh1Lp06d6N+tA0eXp9Ty0jEy/f+FW2pvF5T79ygUhVIHhX7KYkS6poXJ2yRlpeEXf24lIfoh588/4+TJk1hbW3Pt2jVGjBihmRbcsWNHEhISGDBgQJa9B5IkpTA1NWXfvn3Y29vTuHFj9u/fT+3atbUd1n+WQqFgz549jBgxgujoaHx8fChUqBB2dnb88MMPtG/fPlX7tyUj3N3dqVSpEl5eXgDMmTOHsLAwXFxcNAnalStXYmlpCaSUToiLi9P0Y2Jiws8//0z79u1JSkqiTp06eHp6ara7uLjQp0+fVCNiZ86cSUhICJMmTeLgwYMsWbIkTamLt0nXhg0bcvjwYXbv3p2mjvK//TtZvW7dOp48eUL37t1TvX716lUiIiJSLTD33XffpWrj6upK//79AXBzc+PHH3/E2dmZly9fakZkKxQKdu7cycWLF1m9erVmXzMzMwYMGIC7uztCCFq0aKH52UhOTsbW1pahQ4dSoECBD56PlPnWrl1Lnz59mD59OuPHj9d2ODnC1z7zRcpa+/btY9SoUWzYsIG2bdsycuRIDh8+jK+vLyVKlMDb25sRI0bQo0cP9u/fz4YNGzAzM8PW1hZra2t69+5NDff9hJ/YQWLELQAUOnoU67sU3Twpi5S+b8G5t2KC1qFXsBTG1VuS9OQe0XsWUMRpNga6emnaPnr0SDNquWrVqjg5OfHrr7+ip6dH165dNe3KlSunqVMvSZKUlRTpmbajUCjEf2F6jyRJ2rVp0yacnJwwMDBIMyLrn4QQvHyTgF7B0pg5z3tnmzf3LvB44ySKOM3GsES1Dx5XqQDTiDP8ZFOLdu3a4e/vz8qVK9m7dy+mpqb0798fZ2dnjh8/zpkzZ1i+fPlnnackSemXlJRE79698fPzw8/PDwsLC22HJP2DWq1+b1InISFBUy/3bf3c+Ph4kpKSUKlUmjIJefLkQU8v5eY5OTmZV69eaW6aIeUzPyIiguTkZExNTcmbN69mW0REBPnz509VzzcyMpK4uDgSEhLInTs3ZcuWzZKkU2hoKElJSVStWjXV69HR0Vy7di3V9+rbkW1v+fn50aFDB02i+e7du5w6dYqKFStSt25dTbuzZ88SHx9P48aN0xx/27ZtxMXFYWdnh7FxSo385ORkFi5ciKura5ramVLWWrJkCW5ubnh4eDBkyBBthyNJ/wlvk8JXrlxh6NChKBQK7t69i5+fHyVKlGDr1q3ExMRw+vRpfvrpJyIjIzEzM6NLly4EBgZiZGRE9KtEEt+8JlfFBhTs8CP353dDqasP//98Vie8xriGJflb9U9z/JeXD/EswJOiveejV6Ak6qR4ItcMx7BUdSxdJrJ9UKNU7desWcPq1as5evQosbGxTJ48GQ8PD4oVK5aqhrCxsTHbtm2jePHi/z6kJElSuv1/EMQHL4JlUliSpBzp7L1n2P9+Ms1KwRmhALYM+B7zMvlTvf748WM2btyIl5cXISEhmJub4+zsjKOjoxyBJUlfkFqtxtXVlVWrVrF169Y0o1IlSZK0adasWUyaNImVK1fi7Oys7XAk6T/jn0lh+HuhuX8mhf+50NzbpLCdnR3NmjVDV1eXxSceEhHxiMTHd/6fFO6adqRwwqtUSWEh1MSe3ErM8fUUbD+c3N8002xLin5A1NpR1GjYnGD/zeTKlUuzrVOnTtSsWZPChQszbdo0rKysCA0NpWLFinh4eLB9+3Z++eUXvLy8+P7777/MmyhJ0lcrPUlhWT5CkqQcybxMfvo0LotX8D2S1RlPDesqFfRuWCZNQhhS6pq6ubnh5ubGlStX8PLyYsaMGYwYMYL27dvj7OyMlZWVHI0lSVlMqVSyePFi8ufPj42NDWvXrsXR0VHbYUmS9B8nhGDixIn89ttvbNq0iS5dumg7JEn6T1Gr1Zp/BwYGcuLECc1sw7fbnj59yvXr14GU2SpbtmwhODiYbdu2oVQqyZXbhDdJanKVT1nEGiGIWjcKFG9HCr/C+NuUhVWFWsWb0DPEHPch+XkkBTuOJHfV1DOY9AqUpHC3afzlO406derg4eGBpaUlQUFB7N27l7lz56JSqahXrx558uTh4sWLjB49moIFC6Knp4e7uztKpZLQ0FAUCgWFChVKU7dekiQpsyg/3kSSJCl7Gt+2Ci2qFEZXmbFpwbpKBS2rFGZc2yofbfvtt98yZ84cHjx4gK+vL4aGhjg6OlK8eHHc3Nw4e/ZsmvrFkiRlHoVCwfTp05k9ezZOTk6p6spKUmbx9/cnMTFR22FIOcDbGQwLFixg165dMiEsSVrw5s0bzb8vXbrEtWvX6NevX6ptz58/p0mTJhQpUoQaNWrQu3dvFAoFI0aMICEhgZexMVRu3wfUyQDo5MpDUef5lBi8ihKDV5HHvBNClQRA8ovHRO9ZiNIgN0X7eKRJCENKWbradetx/tw58uTJw6xZs0hOTqZGjRosWrSIihUrsmzZMho1akTHjh3x9fVl2rRpXLp0CQ8PD4KDg+nVqxe1atXiu+++05RakiRJygqyfIQkSTlaskrNzH03WHn8LkoFpGfQsFIBQkDfxmUZ17YKujoZez4WExPDli1b8PLy4sSJE1SrVg1nZ2ecnJxSLXokSVLmWr16NS4uLri7uzNhwgS5SJH0yYQQJCenJAAUCgVKpRKlUomDgwPLly/HxMREyxFK2VlycjL9+vVj+/bt+Pn50aRJE22HJEnSv1y+fBlvb2/Wr1/Pw4cPad26NU5OTtjY2GjqsL/1KWXpkuOeomtS8L3b/1mWTq1WExcXl6pOPqR8hqhUKgwMDD54LJVKlWahVEmSpPSSNYUlSfrPOHPvGT/tusq1yFj0dBQkqdJ+Zr19vVrRPEzr9M07S0Zk1F9//cXatWtZu3Yt9+/fx9LSEmdnZ6ytrTEyMsq040iSlGLHjh04ODjg6urKnDlzZGJYSrfw8HDOnz/PnTt3iI2NRalUUrRoUSwtLSlZsqS2w9NQq9UkJCRgYGCgWZBO0r6EhAR69OhBYGAg+/fvx9zcXNshSZL0f+Hh4axfvx5vb28uX75M3bp1cXJywsHBATMzsw/uO93/WqaVpZvU/sOLXEuSJH0JMiksSdJ/zuWHL/C7FMHZsOeEPn5JQrIKA10dKhQ2xrx0PjrUKEb14qYf7yiD1Go1x44dY+3atWz5H3v3GRDVua5h+AYREaxYaCoK9h6xS0dBBUWwoNjFWKJGY09MrHHbY+wNoyiCEhVRiqJIs0Wwl9jAglLsXalzfnicsz1J9k4BFuW9/iSMw1rPsgDzzree7+ef0dDQoHfv3gwePBhLS0sZXAmRiyIiInB1dcXDw4MNGzaoewSFsoYPH87Lly8JCAhQOspvhISEMG/ePCwtLalTpw56enq8f/+es2fPEhAQwE8//UT37t2Vjgl8yHrmzBlmzpypdBTxv96+fYu7uzsXLlzg8OHDNG7cWOlIQhR7L168YM+ePfj6+hIVFYWpqSkDBgygf//+1K//3yviPsrKzuELv7Mcvfbwbw2GP9bSrfFs8bfvQhRCiNwkQ2EhhFDQ27dvCQwMxMfHhyNHjlCzZk0GDRrEoEGDMDMzUzqeEEXCL7/8QpcuXbCzs8PPz++/3oop8l5QUBB9+/bl8ePH6OnpKR3nE8bGxly7du13N+0JCQlh7ty5xMbGFogNRNu2bYu9vT3/+te/lI4igJcvX+Li4sK9e/c4cuQItWvXVjqSEMVWRkYGBw8exNfXl/3796Orq4uHhwcDBgygffv2f3sRhpK1dEIIkdv+zFBYvmIJIUQe0dXVpX///oSHh3Pv3j1GjBjBrl27MDc3x9rams2bN/Py5UulYwpRqLVp04aYmBhOnjyJi4sLr1+/VjpSsefo6IimpiaHDh1SOspvVK5cmYiICF68eEF6ejpZWVlkZmaSlZXFnTt3qFKlSoHob3z48CGnT5/G2dlZ6SgCePLkCQ4ODqSlpREbGysDYSEUoFKpOH78OKNHj8bIyIg+ffqgUqnYtWsXqamprFu3jg4dOvyju/K0SmjynXNDfh7ZjvqGH948LFni94/38fH6huX4eWQ7vnVuKANhIUShIyuFhRAiH6lUKuLj4/Hx8cHf3593797h5ubGoEGD6NixY4EYRghRGN2+fZuOHTtSpUoVQkND0dfPvc5w8df17NmT0qVL4+vrq3SUT0RFReHh4UHbtm1p1qwZFSpU4NWrV5w+fZobN24QEhJC3bp1lY6Jj48PEydO5OHDh/J9QWEpKSl06tQJLS0twsPDqVq1qtKRhChWrl27xo4dO9ixYwd37tzB1taWAQMG0LNnz99s4JbblK6lE0KIf0LqI4QQogBLT08nNDQUHx8fQkJCqFq1KgMGDGDw4ME0bCgbVAjxV6WkpODk5IRKpeLQoUMYGxsrHanY2rFjB2PGjOHhw4cFoorh3717947Q0FDu3r1Leno6ZcuWxcLCgnbt2ikdTa13797o6Oiwfft2paMUa3fu3PnkzaaKFSsqHUmIYiEtLY2dO3fi6+tLfHw8jRs3ZuDAgfTr169AbQgqhBAFmQyFhRCikHj06BH+/v74+Phw9uxZLCwsGDx4MP369aNy5cpKxxOi0Hj69CnOzs6kpaVx+PBhzM3NlY5ULD1//pwqVaoQHByMk5OT0nEKlYyMDCpXrszGjRvp27ev0nGKrevXr9OxY0fq1q1LUFAQZcqUUTqSEEXa69ev2bdvH76+vhw+fBgjIyM8PT0ZMGAATZs2VTqeEEIUOjIUFkKIQujy5cts27YNX19fHj9+jLOzM4MGDcLZ2bnArbgToiB68+YNbm5uXLp0ifDwcJo0aaJ0pGKpc+fO1KxZk/Xr1ysdpVA5evQojo6OPHr0SFamKuT8+fM4OjrStm1bAgIC0NHRUTqSEEVSVlYWR44cwdfXl8DAQLS0tOjVqxcDBgzA2tpa6nOEEOIfkI3mhBCiEGrcuDGLFy/m3r177N+/Hx0dHTw9PTE2NmbcuHHEx8cjb9QJ8cf09PQ4cOAAlpaW2NjYcOrUKaUjFUtubm7s27eP7OxspaMUKsHBwVhaWspAWCEnT57Ezs4OBwcH9uzZIwNhIXKZSqUiLi6OCRMmYGJiQrdu3Xj16hVbt24lNTWVzZs3Y2dnJwNhIYTIBzIUFkKIAkpLS4vOnTvj7+9PamoqCxcu5Pz587Rq1Uo9OH7w4IHSMYUokEqVKsXOnTvp2bMnDg4OHD58WOlIxY6rqysPHz4s8EP5Cxcu0LZtW7KyspSOAnwYCjs7Oysdo1iKiIigU6dO9OrVC19fX0qWLKl0JCGKjMTERObNm0f9+vVp3bo18fHxzJkzh9TUVIKCgujduzelS5dWOqYQQhQrUh8hhBCFTEJCAtu2bWPbtm3cu3ePjh07MnjwYHr06IGurq7S8YQoUFQqFdOmTePHH3/E39+fnj17Kh2pWLG0tKRt27YsXbpU6Sh/KDo6GltbW7Kzs9HUVHa9xI0bN6hXrx5Xr16lQYMGimYpbg4cOEDv3r0ZM2YMS5cuRUPjP95tKYT4E548eUJAQAC+vr6cOHGCunXrMnDgQDw9PTEzM1M6nhBCFGlSHyGEEEWQubk5c+bMISEhgcjISKpVq8aoUaMwNDTEy8uLmJgYqZcQ4n9paGiwePFi5s6dS58+ffjpp5+UjlSsuLm5ERgYWKC/JmVnZ6OhoaH4QBggJCQEMzMz6tevr3SUYsXf3x93d3e+/vprGQgL8Q+9e/eOgIAAunfvjqGhIbNnz6ZVq1bExcVx7do1vv32WxkICyFEAaH8T79CCCH+Fk1NTaytrdm8eTOpqamsX7+e+/fvY2tri7m5ObNnzyYxMVHpmEIUCNOnT2ft2rV8/vnnLFu2TOk4xYabmxuJiYlcvHhR6Sh/KCsrCy0tLaVjAB+qI1xcXGQomY82bdrEgAEDWLRoEbNmzZLfeyH+huzsbI4ePcqwYcMwMDBg6NChlC9fnuDgYB48eMCPP/5Iy5Yt5d+XEEIUMDIUFkKIIkBXVxdPT08OHTpEUlISI0eOJCAgAHNzc6ysrPD29ubFixdKxxRCUSNHjsTPz4/p06czY8aMAr16tagwMzOjWbNmBAYGKh3lD2VlZRWIDY1evnxJTEyM9Annox9++IFRo0axfv16Jk6cqHQcIQqdixcvMnXqVExNTenUqRMpKSmsW7eOtLQ0tm/fjpOTU4F5000IIcRvyVBYCCGKGBMTE6ZNm8aVK1c4ffo0zZs3Z9q0aRgaGuLp6cnBgwfJzs5WOqYQivDw8GD//v0sX76cMWPGkJOTo3SkIs/NzY29e/cqHeMPZWdnF4ihRXh4OKVKlcLGxkbpKEWeSqVi9uzZTJs2jR07dvD5558rHUmIQiMpKYlFixbRpEkTmjVrRmRkJFOmTOHBgweEhYXRv39/ypQpo3RMIYQQf4IMhYUQoojS0NCgVatWrFq1ipSUFPz8/Hj79i3dunWjevXqTJ06lStXrigdU4h816VLF8LDw/Hz86N///5kZGQoHalIc3d359KlSyQkJCgd5XcVlPqI4OBgHB0dKVWqlNJRijSVSsWkSZNYuHAhe/fupW/fvkpHEqLAe/78OZs3b8bOzg5TU1M2bNiAm5sb165dIy4ujvHjx2NoaKh0TCGEEH+RDIWFEKIY0NbWxs3NjX379pGSksLXX3/N0aNHady4MS1btmTVqlU8fvxY6ZhC5BtLS0uioqI4evQoPXr04O3bt0pHKrIaN26Mubl5ga2QKAj1ETk5OYSGhkp1RB7Lzs5mxIgRbNy4kdDQULp166Z0JCEKrIyMDIKCgujduzeGhoZMnTqVBg0acOzYMRISEpg7dy716tVTOqYQQoh/QIbCQghRzFSuXJlx48YRHx/P5cuXsbe3Z8GCBRgZGdGjRw8CAwNl5aQoFpo3b05sbCxXrlzBycmJ58+fKx2pSNLQ0CjQFRIFoT4iLi6OR48e0bVrV0VzFGWZmZn079+f3bt3c+TIEezt7ZWOJESBk5OTw7Fjxxg1ahSGhoZ4eHigqanJzz//TEpKCmvXrqV9+/ayYZwQQhQRMhQWQohirFGjRixevJh79+4RHBys3rDOyMiIsWPHEhcXJ5txiSKtbt26HD9+nMePH2NnZ0daWprSkYokd3d3Tp48SUpKitJRfqMg1EcEBwfTsmVLjIyMFM1RVL1//x53d3eOHj1KZGQkbdu2VTqSEAXKtWvX+PbbbzE3N8fa2pqbN2+ybNky0tLS2LVrF926dUNbW1vpmEIIIXKZDIWFEEKgpaWFk5MTfn5+pKamsmjRIi5evEjr1q1p1KgRixYt4sGDB0rHFCJPVKtWjdjYWLS0tLCysuLu3btKRypy2rRpg5GREUFBQUpH+Y2CUB8REhIi1RF55PXr1zg7O3P+/HliY2Np3ry50pGEKBBSU1P58ccfadmyJQ0aNODAgQN88cUX3Lt3j4iICIYOHUr58uWVjimEECIPyVBYCCHEJ8qXL8/w4cOJiYnh1q1b9OnThw0bNlCjRg2cnJzYsWOH9K+KIqdy5cpERERgYmKCpaUl165dUzpSkaKpqYmrq2uBrJBQuj7iwYMHnDt3DhcXF8UyFFXPnj2jU6dO3L17l2PHjkn/qSj2Xr9+zfbt23FycsLExIRly5bRsWNHLl68yIULF5gyZQrVqlVTOqYQQoh8IkNhIYQQf8jc3JzZs2dz69YtoqKiqF69OqNHj8bQ0BAvLy9iYmLIyclROqYQuaJcuXKEhYXRokULrKysOHPmjNKRihR3d3ciIyN59uyZ0lE+oXR9REhICIaGhrRo0UKxDEVRWloatra2vHr1itjYWExNTZWOJIQisrKyCAsLo3///hgYGDB27FiqV69OREQEd+/eZeHChTRp0kTpmEIIIRQgQ2EhhBD/laamJlZWVnh7e5Oamsr69eu5f/8+dnZ21K5dm1mzZpGQkKB0TCH+MR0dHfbs2UOXLl2ws7MjKipK6UhFhq2tLWXKlCE4OFjpKJ9Quj4iJCSErl27oqkpP5bnlqSkJKytrdHW1iY6Olq6mkWxo1KpOH36NOPHj8fExARXV1fevHmDj48PaWlpeHt7Y2trK193hBCimJPvAkIIIf6Sj5vRHTp0iHv37jFq1Ch2795N7dq11YPjFy9eKB1TiL9NS0uLrVu3MmTIEDp37syBAweUjlQklCxZkm7duhEYGKh0lE8oWR/x7t07jhw5ItURuejWrVtYWlpiaGhIREQElSpVUjqSEPkmISGBuXPnUq9ePdq0acPZs2eZO3cuqamp7Nu3j169eqGjo6N0TCGEEAWEDIWFEEL8bSYmJkydOpXLly8TFxdH8+bNmT59OoaGhvTr14+DBw+SnZ2tdEwh/jJNTU1WrFjB119/jZubG76+vkpHKhLc3Nw4ePBggeolV7I+IioqiqysLDp27KjI+Yuay5cvY2VlRcOGDQkLC6NcuXJKRxIizz1+/Ji1a9fSvn17ateujZ+fH4MHDyYxMZHY2FhGjhyJvr6+0jGFEEIUQDIUFkII8Y9paGjQsmVLVq1aRXJyMv7+/rx//55u3bpRvXp19eBYiMJEQ0ODWbNmsWzZMgYPHsyqVauUjlToOTk5AXDo0CGFk/wfJesjQkJCsLGxoWzZsoqcvyiJi4vDxsaGDh06EBQUhK6urtKRhMgz7969Y9euXXTv3h0jIyPmzp1LmzZtiI+P59dff2XGjBnUqlVL6ZhCCCEKOBkKCyGEyFXa2tr06NGDwMBAUlJS+Prrr4mMjKRJkyZYWFiwcuVKHj16pHRMIf608ePHs2XLFr766ivmzp2LSqVSOlKhpaurS+fOnQtUhYRS9REqlYrg4GCpjsgFMTExODg40L17d3bu3Im2trbSkYTIddnZ2URERDB06FAMDAzw8vKiQoUKhISEcP/+fZYvX46FhQUaGhpKRxVCCFFIyFBYCCFEnqlcuTLjxo0jLi6Oy5cv07FjRxYtWoSxsTGurq7s3buXjIwMpWMK8V8NGjSIPXv28K9//YuvvvqKnJwcpSMVWm5ubhw4cIDMzEylowDK1UdcuXKFu3fv4uzsnO/nLkoOHjyIk5MTgwcPZvPmzYpVgQiRF1QqFRcuXGDKlCnUqFEDJycn0tLSWL9+PWlpaWzbtg1HR0f5ey+EEOJvkaGwEEKIfNGoUSMWLVrEvXv3CA4ORk9Pj/79+2NkZMTYsWOJi4uTFZiiQHN1dSUsLIzNmzczbNgwsrKylI5UKLm4uPD69WuioqKUjgIoVx8REhJC/fr1MTc3z/dzFxW7d++me/fuTJw4kZUrV6KpKS9tRNFw7949Fi5cSNOmTWnevDkxMTFMnz6d5ORkQkND8fT0RE9PT+mYQgghCjn5yUkIIUS+KlGiBE5OTvj5+ZEyI3mkAAAgAElEQVSWlsbixYu5ePEirVu3plGjRixcuJD79+8rHVOI32VnZ0dkZCTBwcH06tWL9+/fKx2p0KlYsSJ2dnYFpkJCqZXCUh3xz2zdupW+ffsyb9485s+fL7fMi0Lv+fPneHt7Y2tri6mpKZs2bcLd3Z3r16/zyy+/MG7cOKpWrap0TCGEEEWIDIWFEEIoply5cnh5eRETE0NCQgIeHh5s2rSJGjVq4OjoyI4dO3j79q3SMYX4RMuWLYmNjSU+Pp6uXbvy6tUrpSMVOm5ubuzbt69A1HAo0Sn85MkTTpw4IUPhv2n16tV4eXmxcuVKpk2bpnQcIf629PR0AgMD6dWrFwYGBkyfPp1GjRpx4sQJbt26xZw5c6hbt67SMYUQQhRRMhQWQghRIJiZmTFr1ixu3bpFdHQ0NWrUYPTo0RgYGDBs2DCio6MLxABJCIAGDRpw/PhxkpKScHBw4MmTJ0pHKlR69OhBamoqv/zyi9JRFKmPOHToEGXLlqV9+/b5et6iYMGCBUyYMIEtW7bwxRdfKB1HiL8sJyeH2NhYRo4ciaGhIZ6enmhpabFnzx6Sk5NZs2YN7dq1k9XvQggh8pwMhYUQQhQoGhoaWFlZ4e3tTWpqKhs3biQ5ORl7e3vMzc3Vg2MhlGZqasqxY8fIyMjA2tqaBw8eKB2p0DAyMqJt27YFokJCifqI4OBgOnfuTMmSJfP1vIWZSqVi+vTpzJo1i4CAAAYNGqR0JCH+kl9//ZUZM2ZgZmaGjY0NCQkJLF++nLS0NHbu3ImLiwva2tpKxxRCCFGMyFBYCCFEgaWrq0u/fv04ePAgSUlJjB49mt27d1OnTh0sLS3ZtGkTL168UDqmKMYMDAyIioqiYsWKdOjQQd6w+Avc3NzYu3ev4htM5nd9RFZWFmFhYVId8Rfk5OQwduxYVq5cyYEDB3B3d1c6khB/SkpKCj/88AMWFhY0bNiQkJAQxo4dS1JSEkeOHGHIkCGUK1dO6ZhCCCGKKRkKCyGEKBSMjY2ZOnUqly9fJj4+nhYtWvD1119jaGhI3759CQsLIysrS+mYohiqUKEC4eHhNGjQAEtLSy5cuKB0pELBzc2NhIQELl++rGiO/K6POHnyJC9evKBz5875ds7CLCsriyFDhuDr68uhQ4dwcnJSOpIQ/9GrV6/Ytm0bjo6OVKtWjR9//BFHR0cuXbrE+fPnmTx5MiYmJkrHFEIIIWQoLIQQonDR0NDAwsKClStXkpycjL+/PxkZGbi6ulK9enWmTJmi+JBJFD+6uroEBQVha2uLjY0Nx48fVzpSgVe7dm2aNGmieIVEftdHBAcH065dOypXrpxv5yys0tPT6dOnD6GhoRw9ehQrKyulIwnxuzIzMwkNDcXT0xMDAwO+/PJLTE1NOXr0KHfu3GHBggU0btxY6ZhCCCHEJ2QoLIQQotDS1tamR48e7N27l+TkZGbMmEFUVBRNmjTBwsKCFStW8OjRI6VjimJCW1ubHTt20LdvXzp16sTBgweVjlTgfayQUFJ+10cEBwdLdcSf8ObNG7p3786pU6eIjo7GwsJC6UhCfEKlUvHLL78wbtw4TExM6NGjB+/fv8fX15fU1FQ2bdqEjY0NmpryklsIIUTBJN+hhBBCFAmVK1dm7NixxMXFceXKFTp16sTixYsxNjbG1dWVvXv3kp6ernRMUcSVKFGCdevWMX78eLp3786uXbuUjlSgubu7c+HCBRITExXLkJ8rhW/fvs3Vq1dxdnbOl/MVVh/rNW7cuEFsbCyNGjVSOpIQardu3WLOnDnUrVuXtm3bcuHCBb7//ntSU1PZu3cv7u7u6OjoKB1TCCGE+K9kKCyEEKLIadiwIQsXLuTevXuEhIRQpkwZBgwYgLGxMWPGjOH06dOKb24lii4NDQ0WLFjA/Pnz8fT0ZOPGjUpHKrCaNm1KrVq1FK2QyM9O4ZCQEKpXr06TJk3y5XyF0ePHj7G3t+fx48fExsZibm6udCQhePToEWvWrKFdu3bUqVOHXbt2MXToUG7fvk1MTAwjRoxAX19f6ZhCCCHEXyJDYSGEEEVWiRIlcHR0ZMeOHaSmprJkyRIuX75MmzZt1IPj+/fv/6ljxcbGMmHCBPbt25fHqUVRMWXKFDZs2MDo0aNZtGiR0nEKJA0NDdzd3RUdCudnfcTH6ggNDY18OV9hk5ycjI2NDTk5OURHR1OtWjWlI4li7O3bt+zcuRMXFxeMjY35/vvvad++PWfPnuXKlSt888031KxZU+mYQgghxN8mQ2EhhBDFQrly5Rg2bBjR0dEkJCTQt29fNm3aRI0aNejUqRO+vr7k5OT85vNycnJYt24dHh4elCpVih9++IGzZ88qcAWiMBo+fDi7du3iu+++Y9q0abJC/Xe4ublx4sQJUlNTFTl/ftVHvH79msjISKmO+AO3b9/GysqK8uXLExkZSdWqVZWOJIqh7Oxsjhw5wpAhQzAwMGD48OFUqlSJ0NBQ7t+/z7Jly/jss8/kjR0hhBBFggyFhRBCFDtmZmbMmjWLW7duERMTQ82aNfH39//d58bHxxMbG8uCBQtYtGgRgwcPZvPmzfmcWBRmvXr1Ijg4mNWrVzNy5Eiys7OVjlSgtGvXjqpVqxIUFKTI+fOrPiIiIoISJUpgb2+f5+cqbK5du4aVlRW1atUiPDycChUqKB1JFFKZmZns3buXiIiIP/05KpWK8+fPM3nyZKpXr07nzp159OgRGzduJC0tDR8fHzp16pRvNTNCCCFEfsm/rZaFEEKIAkZDQwNLS0ssLS3Jysr63ecEBQXRsGFDevXqBcCbN29o1qwZ8OGF5O+tFsrJyZHdxsUnHB0dOXLkCF27duX58+f4+vqira2tdKwCQVNTkx49ehAYGMjIkSPz/fz5VR8RHByMg4MDpUuXzvNzFSbnzp3D0dGR9u3bs2vXLtmgS/wjjx49Yvr06Rw6dOi/Pvfu3bv4+fnh6+vL1atXadOmDd988w19+vSRlepCCCGKBXnFKoQQQgBaWlq/GeQ+e/aMFy9e0Lx5c/T09Hjz5g2ZmZnqYd6/D4Q/1gLcv3+ffv36YW1tzd69e/PvAkSB165dO6Kjo4mJiaF79+68efNG6UgFhpubG0ePHuX58+f5fu78qI9QqVSEhIRIdcT/c+LECezs7HB0dGT37t0yEBZ/yf79+7l8+bL6Y5VKxePHj6lTpw61atX65PF/l5OTQ3x8PDVr1mTz5s307t2bGzducOrUKcaOHSsDYSGEEMWGDIWFEEKIP5CWlkZ2djampqYAJCYm8vLlSypXrgx8+kLz44A4ICAAExMTOnXqxOLFi7GwsODmzZv5H14USE2bNuXYsWNcv34dR0dHnj17pnSkAsHOzg5dXV1CQkLy/dz5UR9x7tw5UlJSZCj8b44cOUKnTp3w8PBg27ZtlCxZUulIopD4+L138uTJJCYmAh9qIzQ0NAgKCuLJkyf88MMPrF+/nitXrqChofHJngGampq0aNGCuLg4bt68yezZs6lTp44i1yKEEEIoSYbCQgghxB/Q0dHh5MmT1K9fH4C4uDiys7Np0qTJJ8/7+GIzJiaG2NhYRo0axXfffcepU6c4ePAgderUQaVScebMGXx8fHjw4EG+X4soOGrXrs2xY8d4/vw5NjY2im2wVpBoa2vj4uJCYGBgvp87P+ojgoODadasGdWrV8/T8xQWQUFBODs788UXX7B+/XrpahX/UWRkJFeuXFF/r9XQ0CA+Pp5mzZrRtWtXAEqWLElWVhbPnj3j7t27PH/+nIiICKysrLhy5cpv7gTS1NSkZcuWsmGcEEKIYk2GwkIIIcQfyMjIoFy5csTHxxMUFMTOnTtp0aKFeuXwxxeTH//r7+/PxYsX6datGzNnzuTZs2dUqVKFp0+fsmzZMiZNmsTZs2cZN24cKSkpil2XUJ6JiQkxMTGULl0aS0tLbt++rXQkxbm5uREWFsa7d+/y9bz5UR8RHBwsq4T/l5+fHz179uTbb79l8eLFMpQTf+jUqVM0b96c8ePHM3PmTIYNG6b+tadPnxIdHY2vry+bNm3i3r17aGlpcezYMdq1a8f48eP5+eefsbe3Z926dcBvaySEEEKI4k6GwkIIIcQfqFu3Lm5ubnh5ebF+/Xrs7Oxwd3f/zfM+DjWcnZ2JjY1l+/btHD58mNjYWODDi9eWLVuyfPlyVqxYgUqlYvPmzcCHjW6WLVvGzp078+/CRIFQqVIljhw5gqmpKZaWlly5ckXpSIrq3LkzOTk5hIeH5+t587o+Ii0tjbi4OFxcXPLsHIXFxo0bGThwIEuXLuW7776TgbD4xJ07d7h69ar646ioKNzc3Lh48SJLly7l6NGjnDx5EviwerhcuXKcPn2affv24erqSmhoKBYWFhgbG1OpUiUA+vXrR1RUlBKXI4QQQhR4MhQWQggh/oOvvvqKwMBAvL29+frrr5k1axbHjx//5DkqlYqkpCRcXFwwNDSkdevWeHh4sGXLFuBDXYCtrS2fffYZANevX6d58+ZERkYyZswYfv31V9auXUuvXr0U2WhLKKds2bKEhITQpk0brK2tOX36tNKRFKOnp4eTk1O+V0jkdX1EaGgolStXpnXr1nl2jsJg6dKljB49mo0bNzJhwgSl44gCZuHChQwZMoRvv/2WSZMmAbB3715sbGwAqFWrFjVq1CA6OhqAhIQEdHV1mTdvHps2baJ///7Mnj2bnj17fjIEfvz4MRYWFmRlZcmbEEIIIcT/I0NhIYQQ4r+oV68eJiYmwIfVjKVLl/7k1zU0NBg6dCgXL15U9xaePXuW8uXLq59z584dvLy8cHV1pWbNmtjb2zNz5kx69uyJt7c3MTEx3Lt3j2PHjqk/5/bt29y9ezcfrlAoSUdHh4CAALp3746DgwNHjx5VOpJi3Nzc2L9/P5mZmfl2zryujwgODqZLly7FtjdXpVIxc+ZMvv76a/z8/PDy8lI6klDYq1evuH79uvrjq1evcurUKTZv3szMmTM5evQo586dw9zcnD179qBSqbh16xbv3r3j7du3AJiZmdGoUSMqVaqEsbGx+k6DNm3aYGBgwMiRI3FwcGDmzJkMGjQozytihBBCiMJIvjsKIYQQf0G7du1+81h6ejqVK1cmPj6ehg0bsnnzZoKDg4mOjub9+/doaWlhZGSEiYkJT548YdeuXaSlpaGjo4OFhQUAL168wMTEhPT0dAA+//xzcnJySEhIwM7OjhkzZsiL2iJMS0uLzZs3U7FiRbp06cKuXbvo0aOH0rHyXbdu3fDy8iImJgYHB4d8OWde1kdkZGQQHh6urospblQqFRMnTmTdunXs27dPepUF3t7e+Pj4UL58edq3b8+YMWNITEzE0tISc3NzMjMzadKkCa9fv2bSpEkMHjwYS0tLLCwsOHfuHHPmzAE+rBy+f/8+169fp169epw8eZIGDRpQtmxZ9u/fz/Hjx3n48CF9+vRBW1tb4asWQgghCiZZKSyEEEL8Q6VKlWL06NHMnj2bBg0aEBYWxrx582jSpAnLli3j1q1blCpVirlz53Ls2DGuX7+OkZERL1++ZOPGjSQlJbF69WoyMjKoVKkSiYmJlC1bllmzZrFu3Tqio6NJTExU+jJFHtPU1GTZsmXMnDmTXr164ePjo3SkfKevr4+trW2+VkjkZX1ETEwM79+/x9HRMU+OX5BlZ2fz+eef4+3tTVhYmAyEi6lz586pN49MSEggJiaGFStWMGHCBK5cucKePXtwcHBQ9/Xv2LGDyMhIatSoQcuWLQkKCmL06NE0atQIU1NT9Rs4vXv3Rk9Pj2nTpjF06FBmzpxJhw4dgA9VNI6OjgwYMEAGwkIIIcR/IENhIYQQIhfY2Nhw7949wsPD2bhxI2PGjAEgOTlZvYlcTEwMNWvWpGrVqujo6PDDDz+Qk5ODm5sbM2fOxMvLCysrK8zMzFiwYAE1atQgMzMTExMTzp0798n5nj59ypIlS3j58iUgu6oXFRoaGsyYMYMVK1YwbNgwVqxYoXSkfOfm5kZgYCA5OTn5cr68rI8IDg7G0tKSChUq5MnxC6qMjAw8PT3Zs2cPR44cwc7OTulIIh9lZ2ezePFiNDU16d27NyNGjODVq1e8evWKrKwsWrRoga2tLSVKlKBq1aqULl0aMzMzAOLi4ujQoQPDhg1j8+bN1K5dmwEDBtCxY0eaNm1K48aNUalU6Ovrs3z5cr744gsaNGjAqVOnGDVqlMJXLoQQQhQuMhQWQgghclGtWrWoWrWq+uNu3bqxfft2LC0t+de//kXXrl2pUaMGAB06dMDIyIiUlBSWL1+Ou7u7ehVUamoqAQEBTJkyBWNjY1q0aKE+ZmxsLO7u7vj6+vL69Wv14/fv38+nqxR5bcyYMWzbto3Jkycza9asYjX079GjB8nJycTFxeXL+fKqPkKlUhEcHIyLi0uuH7sge/fuHe7u7kRFRREdHU2bNm2UjiTywZMnT9T/f+vWLQIDA7l48SLHjh3j9u3bbN++nfr16zNjxgwALl68SGRk5G/eMFmzZg07d+6kZ8+erFmzRv34u3fvuHr1KsbGxuoN43R1dXF0dGTq1KmYmprmw1UKIYQQRYsMhYUQQog81LlzZ27evMmkSZNYs2YNs2bNUv/a8uXLOXv2LN999x1ffvkl8GGFHXzoGJ46dSrly5dn4cKF1KlTB4CNGzcybdo0UlNTGTVqFMbGxpw9e5Zx48Zhb2+Pvr4+/v7++X+hItf179+fwMBAFi9ezJdffplvK2eVZmJiQps2bdi7d2++nC+v6iNu3LhBQkJCsRoKv3r1iq5du3Lx4kViY2Np2rSp0pFEHgsMDKRGjRoMGTJEXXnz/v17WrZsibm5OYaGhtSrV0/do9+oUSOys7PZv3+/umJp1apVAJw+fVp93Fq1alGtWjXev38PfKiemDFjBllZWfl/kUIIIUQRJTvWCCGEEHlMU1MTNze3Tx47efIkS5YsISQkhGbNmqkf9/Pzw93dnaZNm7Ju3Tq2bNlCZmYmSUlJ+Pr6cvr0aWbMmMH+/fvVm9Rdu3aN6tWrEx0dzcGDB1m3bh1du3blwYMHXLt2DWdnZ0qVKpWv1yxyh4uLCwcPHqRbt248e/aMLVu2ULJkSaVj5Tl3d3e8vb1ZuHChelVgXsmr+ojg4GDMzc2pW7durh+7IHr69CldunTh6dOnxMbGysrNIur58+fq1b0qlYr169ezcOFCunXrRs+ePalatSqWlpbMmzdP3eerr6/Po0eP1McoUaIEs2fPBsDf359t27YxevRoVqxYgZ6eHvXq1cPf358ZM2ago6MDgKura/5eqBBCCFEMyEphIYQQQgHt2rXjwoULfPbZZ+pqgJSUFKZOnapeLaylpcXr1685deoUI0aMwNDQkJCQEDQ0NChXrhwmJibAhxfL06ZNw8jIiI4dO3LixAlUKhU6Ojps2bKF6tWr061bN8LCwhS7XvH32djYEBUVxaFDh3B3d1dv2lSUubm5cfPmTa5evZrn58qr+oiQkBBcXFzyfKhdEKSlpWFra8vbt29lIFxE7du3j1atWtG2bVvmzp3LzZs3SUlJ4c2bN5iamlK2bFm8vLwIDAxEpVJRoUIFSpQowdu3b/nll1/w8PAAPvx7u3//Pi9evACgdOnSlC1blpycHObNm0eLFi04ceIE06dP/82bqUIIIYTIXTIUFkIIIRRSpUoVVCqVeiBlZGTEmDFjcHJyYtasWaxYsYIWLVqQnZ3N2bNn2bNnDwEBAQQHB+Pk5KQeCn9cSQWwcuVKOnXqRLly5TAzM+PAgQPcvXsXa2tr+vfvj4uLCyqViszMTEWuWfw9LVq0IDY2lgsXLtClSxf1BoNFVZ06dWjUqFG+VEjkRX3E8+fPiY2NLRbVEffu3cPKygodHR2io6MxNDRUOpLIBf/eY56ZmUlERATjxo3j0KFD3L17lyVLlqClpUWpUqWoWLEi8OHOhrdv33L+/HlUKhUqlYoXL17w7NkzDAwMgA9vdq5fvx5PT09CQkJYv349bdq0QVtbGzMzM0aNGsWePXvo1auXItcthBBCFCcyFBZCCCEU9P9XEc6aNYtJkybx8uVLpk6dyty5c7Gzs+PBgwcMHjyYpUuX4u3tzdq1a3n48CHw4Vbc9PR0Bg4cyLlz55g7dy6amppkZmaSk5ND6dKlqVu3Lp6enowaNYrExES+/PJLmjRpwpw5c3j16pUSly7+ovr163Ps2DFSUlKws7P75Hbsosjd3Z3AwMA8P09e1EeEh4ejo6ODtbV1rh63oLl58yaWlpYYGxsTERGBvr6+0pHEP5SQkICTkxPOzs74+fkB8Ouvv5KYmMigQYMwNTWlV69eJCUlUbVqVXW9EYCenh516tThzJkzZGRkoKGhwZYtW2jdujX169cH4OXLl0yYMIEuXbqwaNEiOnXqxOjRoxW7XiGEEKI4k6GwEEIIUcB4enqyfPlyrK2t0dTUJCsrC11dXfr378/nn3/OV199haOjI+np6cCH23p79uxJSkoKvr6+tGrVCoCSJUuiqanJli1bWLVqFZ9//jlVq1ZlwoQJGBgYsHfvXuLi4ggICAAoNhuZFWY1atQgNjYWlUqFlZWVehhTFLm5uXHu3Dnu3LmTp+fJi/qIkJAQHB0d1Z2qRdGlS5ewsrKicePGhIaGUrZsWaUjiX/g48rgn3/+GQMDA1auXMnevXvx8fGhadOmpKamcuDAAeDD5nL29vYAWFtbc/DgQfXdC+3atWP//v2UKlWKt2/fEhERQZUqVZg4cSJmZmYsXryYChUqMHbsWGJiYpg0aRK6urrKXLQQQghRzMlQWAghhCjgPq5izMrKIiEhASsrK0aNGoWenh59+vTBx8cHNzc3Dhw4gKGh4Se7sx85coRVq1axfPlymjVrxv79+2ncuDFTp06lTp062Nvbc+zYMZ4+fYqm5ocfC+Lj47l06ZIi1yr+u6pVqxIZGYmBgQEdOnTgxo0bSkfKE82bN8fU1DTPVwvndn1EdnY2oaGhRbo64vTp09jY2GBtbc2+fftkqFcIPX/+nOHDh9OxY0d2796tvmtl3bp1DBw4kNq1azNnzhxOnjzJr7/+ypIlS9i0aRNmZmZcu3aNe/fu8d133/Hll1+SnJxMeHg48KEjODk5mXfv3vHq1StOnDjBzp070dTUJDAwkO+//z5PNnYUQgghxF8nQ2EhhBCikNDS0mLhwoU4Ojry9u1bpk6dyu7du2nUqBEeHh6ULl0aQD3cDQsLw8/Pj1GjRtGkSRMAMjIyuHnzJrq6uuTk5KCjo8PLly/R19cnLS2NadOmYWdnR1hYmHrDO1HwlC9fnoMHD9K0aVMsLS05d+6c0pFynYaGRr5USOR2fcTp06d5/PgxXbp0ybVjFiRRUVE4ODjQo0cP/P39i/Rq6KLm37vId+3axdOnT/nxxx/ZsGEDPj4+ZGZm0rRpUxISEoAP3d6NGzdm48aN2NvbU7duXX766SdiYmJYtWoVGzZs4PXr14waNYoZM2bw/fffs3LlSkaMGEHp0qXR0tLi4sWL3Llzh6VLl9KsWTOlLl0IIYQQv0OGwkIIIUQho62tja6uLt7e3oSEhHDlyhWqVavGwIED1St+c3JyWLBgAdbW1gwcOFD9uZ999hlPnz5l/fr1eHl5ceDAATp37gzAiRMnePPmDfXq1fvdW99v3brFs2fP8vVaxR8rXbo0gYGBdOrUCVtbW2JjY5WOlOvc3Nw4duwYaWlpeXaO3K6PCAkJoVWrVkVyw7XQ0FC6dOnCsGHD8Pb2zvXaDZF39u/fz8SJE7l79y4A/v7+NG/enMaNG/PNN99w9+5dwsPD6dq1K+fPnwc+fK9xcHDg6tWr5OTksHPnTkxNTdXHLFOmDNHR0djZ2bF582ZycnKoUqWKepO4SpUqUadOnfy/WCGEEEL8KTIUFkIIIQqxLl26EBgYSEpKCkOHDqVkyZIATJkyhfT0dIYMGaJeQQzQr18/Bg4ciL+/Pz4+PvTt21c9NHZzc8PExARLS0tMTEzUn/Pw4UOWLFlCz549Wb16df5eoPiPSpYsyfbt2xkwYACOjo6EhoYqHSlXtW/fnipVqrB///48OX5OTg4qlSpXVwoHBwcXyeqIn3/+GVdXVyZPnsyPP/6oviNBFFwqlUrdFX/q1CkOHTrE3bt3ycnJoWnTpurvDe3ataNSpUpcuXKFWrVq8fTpU/XwuGLFihgZGXHp0iWcnJzw8vIiPj6eadOmYWBggKWlJQCWlpbMnDmTtWvXfjI4FkIIIUTBJT/NCSGEEEVA6dKlsbe3p2zZsuTk5GBhYcHSpUuBDx2nHzcRAjA0NCQ5OZk1a9YwePBgdHR01L927NgxWrZsib6+PvBhFeWuXbs4fPgwVatWxdbWVv3cJ0+e8NNPP+Hr65s/Fyl+l6amJqtXr2by5Mm4urri7++vdKRcU6JECVxdXfOsQiI7Oxsg14bCSUlJXLhwAWdn51w5XkHx008/0bdvX+bPn8+8efPU/bOiYPj3TeI8PDwICAggMzMTDQ0NNDU1yczM5MKFC9jb23P8+HE0NTXR19cnPT2dly9foqOjQ+3atXnz5g2lS5fGwMCArVu3AvDgwQNu376NgYEB33zzDdWrV8fd3Z2IiAi++uorWQkshBBCFGLS8i+EEEIUMZqamnh6eqo/LlGihHpoMGnSJK5fv86MGTMYMmQI8H+dqqdOnSIzMxMLCwv1beFaWlqMGzeO7Oxs0tLSMDMzAyAtLY05c+aQmZnJ1atX8fb2xsfHR1aIKURDQ4N58+ZRsWJFBgwYwPPnzxk9erTSsXKFm5sbrq6uvHjxgvLly+fqsT9uyphbNQihoaEYGRnx2Wef5crxCoKVK1fy1VdfsXr16iLzd6qo0dDQ4Pz588yePZtx48YRHx9PfHw8ixcvBiAzM5P37wvycz4AACAASURBVN/Trl07kpKSuH//PhYWFhw+fJgbN27QsmVLatSoQVhYGGXLlsXLy4v+/fuTlZVFpUqVKF++PBUqVMDQ0JANGzaQkZFBuXLlFL5qIYQQQvxTslJYCCGEKAY0NDR4//49L168YMmSJeqBMPzfxnT79u2jadOmVKtWDUB92/GDBw9ITEzE2NhYXStx6tQpdHV12bBhA8ePHwfgl19+AeDZs2cEBQVx8eLF/Lo88b8mTpzIpk2bGDt2LPPnz/9khXhhZW9vT+nSpfOkGiO3VwoHBwfj7OxcJKoVVCoV8+fPZ+LEifj4+MhAuAA5ceIEY8aMYc+ePerH9uzZQ7t27Rg1ahSLFy/m2rVr6tqVgIAAzMzM6NatGyVLluT27ds4OjpSrlw5goKCADA2NiYkJAQ9PT2aN2/Oxo0bSU9PJyQkhC+//FJ9R4mOjo4MhIUQQogiQlYKCyGEEMWEjo4O3t7ewIeBz8dbwDU1NcnOzubMmTOMHz+eMmXKfPJ5Z8+eJTMzk+bNm6sfe/r0KTdu3EBTU5OHDx9Svnx5dZ9x3759qV69OmFhYdja2rJu3ToZIuSjYcOGUaFCBfr168ezZ89YsmRJob7dv1SpUjg7OxMYGEi/fv1y9dgfVwrnxlD43bt3RERE4Ofn94+PpTSVSsW0adNYsWIFP//8M25ubkpHEv8rNTWV8ePH4+joyO7du0lKSmLChAk8fPiQsmXLqp/XvXt3tm7dSvfu3Xn//j2tWrXCxMQELS0tZsyYQZMmTfDy8mLo0KGUKlUKU1NTateuja6uLgAdOnSgdevW6q/rQgghhCh6Cv8yBiGEEEL8Zf9/SHjixAmys7OpX7/+J8NigEuXLlG+fHmaNGkCwOvXr3nw4AF37txh2bJlfPXVV2RnZ+Pg4EBISAglS5bE29ubmzdv8uTJExISEgBITk4mMDBQPYgTecfd3Z3Q0FDWr1/P8OHDC/3vuZubG6Ghobx79y5Xj5ub9RGRkZFkZ2fTsWPHf3wsJeXk5PDFF1+wevVqDhw4IANhBd25c4epU6eyYMEC7ty5A3zodzYwMGD+/PnMnTuXjIwMtm/fjoeHBwcOHFB/roeHB+fOnQNgx44dREZG0rp1a9asWcOzZ8+wsrKiRYsWfP/997x9+5aVK1eq39D7SAbCQgghRNEmQ2EhhBBCYGVlxe7du6lZsybwf7fVX79+nbS0NOrWrUuFChV49eoV8+fPJyUlhRUrVpCYmEjJkiVZu3Yt5cqVw9/fX72i+GPlxPXr13nz5g1bt27F09OTmjVr4u7urq6bKAoVBwWRg4MDERERBAYG4uHhQXp6utKR/rYuXbqQlZXFkSNHcvW4ublSODg4GDs7u9+stC9MsrKyGDx4MH5+foSHh+Po6Kh0pGLr0qVLDBo0iGfPnpGYmMiIESN4+PAhRkZGPHv2DICaNWtiZWXFnj17aNOmDRkZGdy+fRuAsmXLUqtWLQICAnBwcODt27csXryYc+fOMWDAAN6/fw9At27dmD9/PnFxcQwePFix6xVCCCFE/pOhsBBCCCEA0NfXVw/HPq6cvHDhAo8fP6ZRo0YAXL58mVu3buHq6oqdnR0rVqwgMzOTFy9e8O7dOxo3bkxkZCTh4eHMmjWLkiVL4uDgwL59+9DQ0OD+/fskJibSpk0bzpw5oz53UlJS/l9wMdCmTRtiYmI4efIkLi4uvH79WulIf0uZMmVwdHRk7969uXrc3OoUVqlU6j7hwio9PZ3evXtz8OBBIiMjsbS0VDpSsfDxTbGzZ8+qV/bChzfVatasyaZNm/jxxx8pU6YMFy5coHbt2ujp6ZGWlkbJkiVp2rQpOjo6JCQk4OLiwpo1a9THaNq0KdevX2f27NkEBQVha2tL1apVad68Oa1atVI/rzDXywghhBDi75OhsBBCCCH+UJ8+ffj222+xsLAAoFatWqSlpfHo0SMAjh49SuXKlYmPj2fVqlWcOXMGLy8vVq9eTXp6OqtWraJy5cqsWbOG+Ph4EhMT0dbWZtq0aXzxxRdcuHCB8ePH07FjR/T19dmxY4esHM5ljRs35vjx4yQmJtKxY0eePn2qdKS/xd3dnQMHDuRqFUZu1UdcvnyZpKSkQjsUfvPmDd26deP06dNER0fTokULpSMVGxoaGiQnJzN+/Hiio6PVj2tra1O1alWeP3+Onp4eN27cQFtbm/r161OtWjUiIyPVz2vVqhXh4eFMnTqVa9euMXnyZM6cOcPZs2fp3Lkz8OHvenZ2Npqamjg5Oanf6BNCCCFE8SVDYSGEEEL8Rw0aNFCvpDQ0NKRdu3Z8+eWXODo6Mm7cOEqUKIGxsTGnT59m4sSJDBs2DG9vb2rXrs3Tp0+Jj4/H2NiYWrVq0b17d0aNGqVeoXn16lVMTEw4evQoK1asYMOGDTx48AD4MKjKzMxU7LqLklq1anHs2DHevn2LjY0NKSkpSkf6y7p168bz58+JjY3NtWPmVn1EcHAwDRs2xMzMLDdi5avnz5/j5OTErVu3iI2NpWHDhkpHKtKys7NZunQp58+fVz8WFRXF7du3uXv3LsnJyQA0atSIO3fuMHbsWEaMGEGZMmVISUnh1atXWFtbs2PHDuBD9/vTp08pUaIENWvWZPbs2Tx79oxx48bRqFEj9Z+nlpZWrnRnCyGEEKLokKGwEEIIIf6SRYsWcfz4cSwtLZk8eTI//PADtWrVIjY2lipVqgDw7t07YmJiuHv3Lq1atWLr1q0sXbqU6Oho7t+/z5UrVwBwdXVl2rRpmJiY0KlTJ44fP46Ojg5v3rzhu+++o0WLFtjZ2amfL/4+IyMjoqKiKFOmDB06dFBvAFhYVKpUCWtr61ytkMit+ojCWh3x6NEj7O3tefr0KbGxsYVyqF3YlChRggULFnDkyBH1mxJJSUkMGjSIkiVLcvHiRQBq1KjB999/T1JSEhYWFmzfvp3o6GjWrl1Lz549SUxMZMGCBeph/scN4lq2bMmaNWvYv38/69atQ09PT7FrFUIIIUTBJkNhIYQQQvxl9evXZ+bMmXz++ecAVKlSBUdHR7p06cLAgQMZOHAg5ubm1KxZk507d1KmTBnS09OpW7cuZ8+e5e3bt8CHW58/WrFiBY6OjmRnZ/PTTz/x5s0bLl26RIcOHVi1apUi11nU6Ovrc+TIEWrXro2lpSWXLl1SOtJf4u7uzr59+3KtYiQ3Vgo/fvyYU6dO4eLikiuZ8suDBw+wsbEBIDo6GhMTE4UTFQ8nTpwAPgzkP67YnzRpEjNnziQrK4vLly+rn/vgwQPq1avHyJEjqVOnDl27duXWrVtkZGSwevVqkpOTGTRoEIaGhuo/SwAdHR0qV66cvxcmhBBCiEJHhsJCCCGE+McqVqzI9u3bmTt3LtnZ2QwaNIitW7cSERHB+vXrAShVqhRBQUE8f/6ctm3bAh+GcZmZmQwePJi4uDi+/fZbDAwM+OWXX3B3dwegVatWXLt2jRcvXqjPl5SU9MmGSuLP09PT48CBA1haWmJjY8OpU6eUjvSn9ejRg/v37xMfH58rx8uNTuGDBw9Srlw52rdvnyuZ8kNiYiJWVlbo6+tz9OhR9Qp/kXdycnIASEtLo2XLljx9+lS9waaWlhY6OjqYm5uTlJTEnTt3AEhOTiY7O5uwsDDgw0A5PT0dfX197OzsWLlyJTt27CAgIED+DIUQQgjxl8lQWAghhBC5pl+/fvj5+TF8+HD09PSws7MjPT0dT09PJkyYwNy5c/nmm28AUKlUhISE4O7uzr1799i+fTsdOnQAPmzc9XFgt23bNqytrSldujS//vorCxcuxNTUVL3RkmxM99eVKlWKnTt34u7ujoODA4cPH1Y60p9SrVo1WrVqlWsVErlRHxEcHEznzp3/cQVFfrl69SpWVlbUrl2bQ4cOUaFCBaUjFWpv3rxh3rx5/Otf/+L27dvA/w2A/52m5oeXXZs3b+bbb7/F2Nj4Nyv1mzVrRkZGhrpv2NbWlnLlyjF9+nQaNWrEpk2b6NmzJxoaGurPqV27dl5dmhBCCCGKOBkKCyGEECLPGBkZsXPnTmrVqoVKpWL9+vV88803vHr1Cg8PDzZv3oyzszP79+/HyMiIGzdusGzZMmrUqMHs2bNp06YNL1++ZPLkyWhra3PmzBns7e0pUaIEkydPVvryCrUSJUqwadMmxowZg7OzM3v27FE60p/i7u7O3r17c+XNgH9aH5GZmcmhQ4cKTXXE2bNnsbGxoXXr1hw4cED6Zv8hlUrFsmXL1Kut3d3dSU5OVg+A/7/Xr19TpkwZWrRogZubG1euXMHHx4cLFy4A0KRJE/T19bl69SoA1atXZ+nSpUybNo1Zs2aRmprK8OHD1cf79+GwEEIIIcRfVTiWNAghhBCi0DI1NWX+/Pnqj9+8ecOUKVPYvXs3U6dOpX///pQtWxaAwMBA0tLSWLNmDdWrV2f+/PnU+p/27jysyjr///jrHEBww11ACNAUxXLJXcMFNRVEFCMrNDDLtJ9jLpmV1uTWt7Rxz+Vbmhs2qV93EBcwRSXTHJemRCN3BEwRl0RkOb8/mphx3Fg96P18XJdXde7P8j4n87p7nQ/vu2ZNOTo6Kjs7W/369dP+/ftVpkyZ3BYUBCMFZzKZNGXKFFWuXFl9+vTRl19+qQEDBli7rPsKCgrS+++/r6NHj6p+/fqFWquw7SPi4uJ09epVdevWrVB1PAx79uyRv7+/evTooUWLFsnOzs7aJT3ysrKyNHv2bG3ZskVNmjRRWlqaVqxYod69e8vDw0MWi+W2P5+OHj2qxMRE/fTTT5o0aZKio6O1Y8cOLVu2TJJUoUIFVa9ePXecq6urbGxsFBISYq23CAAAHmOcFAYAAA9V2bJlNX/+fG3atEnHjx+Xu7u7QkJClJKSovT0dF24cEEuLi6SJHd3d+3bt++2fsLTp09Xnz59JP37x/9ROO+9957mzp2rgQMHaurUqdYu577q1q2r+vXra+3atYVeq7DtIyIiItS6dWtVqVKl0LUUp23btqlLly4KCQnR0qVLCYSLyLVr19SuXTulpKRIkvz9/XX16lXt27dP0p1fWP3+++86fPiwBg4cqI4dO+ovf/mLhg0bpkaNGuWOefnll/XZZ5/x4D8AAFDsOCkMAACsolu3burWrZvS09O1Z88eVapUSa1atdI333yj//u//5Otra1Wr16tunXrqkKFCrnz1q1bp/3790vSPX9MG/k3aNAgVaxYUf369VNqaqomTZpUYk9hBwUFae3atRo7dmyh1insSeHIyEiFhoYWqobitnbtWr300ksaNmyYJk+eXGL/nT6KypQpo+rVq+u3336T9McXFm5ubjp27JgyMjJkb29/2/h69eopISFB1atXlyTt2LFD0dHRunjxoqpWrSpJudcAAACKG6EwAACwqtKlS6tz586SpPbt2+v111/Xu+++q1KlSqlPnz4aOHBg7tj169erSpUqeuqpp+740WwU3osvvihHR0c9//zzunz5sj7//PMSGbwHBQXp448/1unTp+Xh4VHgdbKysmQ2mwv0++jXX3/V0aNHS3Q/4fDwcPXv31/jx4/XmDFj+O+liDk4OMjT01OJiYlKTU1V5cqVVb16df3yyy86e/bsHQ+Bc3Z2lvTHg+hMJpPatWunDh06WKFyAAAA2kcAAIASpHTp0ho1apROnz6tnTt36uOPP5anp2fuic6//e1veuONNyTprg8ay8zMfKj1Po78/Py0detWff311+rXr1+J/EybNGkid3d3rVu3rlDrZGdnF7h1RGRkpNzd3fXUU08VqobiMn/+fIWFhWnatGkaO3YsgXAx6d69u5KSkhQXFydJKleunL777jvVrl1b165dU2Ji4h1z/vwioiR+4QIAAIyDOxEAAFAi1ahRIzf4tbW1VU5Ojvbv369BgwZJurN1REZGhj7++GO1bt1a8+fP1+XLlx96zY8LHx8f7dixQzExMerVq5du3Lhh7ZJuYzKZcltIFEZWVlahQuGAgIASGbZOmTJFQ4YM0Zdffqm33nrL2uU8ViwWy21fSHl7e6tZs2b661//qv379+vs2bO5fzYNHz5c33//vXJycqxVLgAAwD2Z7nbK5o5BJpMlL+MAAACKU05Ojsxmc+5f/9vPP/+sL7/8UsuXL9eVK1cUGBiosLAwde3alYdrFcDx48f13HPPyd3dXRs3blTFihWtXVKunTt3qmPHjkpOTla1atUKtMb69ev1yiuv6OrVq/mad+3aNVWtWlXr1q2Tn59fgfYuDhaLRR9++KEmT56s5cuX5z6QEYV38uRJLV++XJmZmRo7dqxKlSp12/WhQ4cqJSVFP/74o6ZMmaIePXrQ4gYAAFiNyWSSxWK5740IJ4UBAMAj488g+F4/dl2/fn1Nnz5diYmJWr16tSTp+eefl5ubm0aOHKnDhw8/tFofB15eXtqzZ48uXrwoX19fpaSkWLukXD4+PqpSpYo2bNhQ4DUK2j4iOjpaNjY2JaofbE5OjoYPH66pU6dq/fr1BMJF4NKlS5o/f758fHxUq1YtLVu2TI6Ojnf9gmn27NmaMWOGvv/+e/Xo0UOSCIQBAECJRigMAAAeO3Z2dgoICNCqVauUlJSkcePGKS4uTo0bN1ajRo00bdq0EhVwlmRubm7atWuXbGxs1LZtW50+fdraJUmSbGxsFBgYqDVr1hR4jYK2j4iMjFTnzp1VunTpAu9dlLKzs/X666/rq6++UlRUlPz9/a1d0iMrPT1dq1atUq9eveTi4qKPPvpIzZo10/79+xUfH6+33377nmFvjRo15Ojo+JArBgAAKBhCYQAA8FirXLmy3nzzTe3du1fx8fEKCAjQjBkz5Orqmhsc37x509pllmhVq1bV9u3b5erqKh8fH8XHx1u7JElS7969FR0dne/2D3/KysqSjY1Nvubk5OTk9hMuCW7duqWXX35Z69atU0xMTIk6vfyoyMnJ0bfffqvXXntNzs7O6t+/v8qVK6cNGzYoMTFRM2bMULNmzTj5CwAAHiuEwgAAwDDq1q2rjz/+WKdOndKWLVtUuXJl9e/fXy4uLho8eLC+++478RyFu3N0dFRUVJSaNGmitm3b6sCBA9YuSZ06dZK9vb2ioqIKNL8g7SP+8Y9/KDk5uUScxk1PT1evXr0UGxurnTt3qkWLFtYu6ZHy448/6t1335WHh4c6d+6s8+fPa+7cuUpJSVF4eLi6detW4AcRAgAAlHSEwgAAwHDMZrM6deqkpUuXKiUlRTNmzNDx48fVpk0b1a1bV5MmTSoxbRJKEgcHB61evVp+fn7y9fXVjh07rFqPvb29/P39C9xCoiDtIyIjI9W4cWO5ubkVaM+icvXqVfn5+emf//yndu3apQYNGli1nkfFuXPnNGXKFDVs2FANGzbU9u3bNWrUKCUmJioqKkp9+/ZVuXLlrF0mAABAsSMUBgAAhlauXDmFhYVp+/btOnXqlEJDQ7V06VJ5enqqY8eOWrJkia5fv27tMksMW1tbLV68WP3791e3bt20ceNGq9bTu3dvbdq0qUAtQArSPiIiIsLqrSNSU1NzT7bu3r1bderUsWo9Jd2VK1f01VdfqWPHjnJ3d9e8efPUs2dPHT16VPv379ewYcPk7Oxs7TIBAAAeKkJhAACAf/Hw8NAHH3ygY8eOKS4uTl5eXho+fLicnJwUGhqqmJgY5eTkWLtMqzObzZo5c6bef/99BQUFKTw83Gq1+Pn5KTMzUzExMfmem9/2EUlJSfrhhx/UvXv3fO9VVJKTk9W+fXvdvHlTu3btkru7u9VqKclu3bqlDRs2qE+fPnJyctI777yjevXqaffu3Tpx4oQmTpyoevXqWbtMAAAAqyEUBgAA+C8mk0mtW7fW/PnzlZSUpEWLFik1NVVdu3aVp6enxo4dq2PHjlm7TKsymUz66KOPNHXqVIWFhenzzz+3Sh3ly5dX586dC9RCIr/tI6KiolStWjU1b94833sVhdOnT6tt27YqU6aMduzYIScnJ6vUUVJZLBbt2bNHb775plxcXNSnTx9J0sqVK5WUlKS5c+eqTZs2PDAOAABAhMIAAAD35eDgoD59+igiIkLnzp3T8OHDFRERoXr16qlVq1aaN2+eUlNTrV2m1QwbNkyLFi3S8OHDNWHCBKs8qK93797asGGDsrKy8jUvv+0jIiIi5O/vn++WE0Xh+PHjatu2rdzc3BQdHa3KlSs/9BpKqvj4eH344Yd68skn1bZtWx0/flyfffaZUlJStHLlSgUGBqpUqVLWLhMAAKBEIRQGAADII2dnZ40cOVKHDx/WwYMH1aZNG40bN04uLi564YUXFBERoczMTGuX+dCFhoZq9erV+p//+R+NGDHiobfY6NGjh1JTU7V79+58zctP+4iMjAxt3brVKv2Ejxw5orZt26phw4batGmTypcv/9BrKGlSUlI0c+ZMNW/eXN7e3tqwYYPefPNNnTlzRjExMRowYIAqVKhg7TIBAABKLEJhAACAAmjcuLGmTZumc+fOafXq1ZKk559/Xm5ubhoxYoQOHTpk5Qofrp49eyoqKkoLFy7UgAED8n1qtzCqVaumdu3aae3atfmal5/2EbGxscrIyNBzzz1XkBIL7Pvvv1f79u3VoUMHrVmzRqVLl36o+5ck169fV3h4uLp166YaNWros88+U8eOHXX48GEdPnxY77zzjtzc3KxdJgAAwCOBUBgAAKAQ7OzsFBAQoFWrVik5OVnjx4/X3r179cwzz6hRo0aaNm2akpOTrV3mQ+Hr66vt27crIiJCwcHBunnz5kPbOygoSGvXrs1X+4r8tI+IiIhQu3btHurp02+//VadOnVS79699fXXXxuyBUJWVpY2b96sfv36ycnJSUOGDMltoXHmzBlNnjxZDRs2tHaZAAAAjxxCYQAAgCJSqVIlDR48WN99953i4+MVEBCgGTNmyM3NTd27d9fKlSsfalBqDc2bN1dsbKx++OEH+fv769q1aw9l3169euns2bM6cOBAnufktX2ExWJRRETEQ20dERkZKX9/f73++uv68ssvrdLH2FosFov279+vYcOGydXVVYGBgbp+/bqWLFmi5ORkLViwQL6+vjKb+V8ZAACAguJOCgAAoBjUrVtXH3/8sU6dOqWtW7eqatWqGjBggJydnTVo0CDFxcVZ5aFsD0P9+vW1Z88enT17Vp06ddKlS5eKfU93d3c1a9YsXy0k8to+4tixYzpx4oS6d+9emBLzbMWKFerVq5dGjx6t6dOnGyb8PHHihCZOnKh69eqpRYsWOnDggMaPH6+kpCStW7dOwcHBhm6fAQAAUJSMcYcJAABgJWazWR07dsw95Thz5kwlJCTIx8dHdevW1aRJk3T69Glrl1nkPDw8tHv3bt26dUvt2rVTYmJise/5ZwuJvMpr+4iIiAjVqVNHXl5ehSkvTxYuXKiQkBB98sknGj9+vEwmU7HvaU2XLl3SvHnz9Oyzz+rJJ5/U8uXLFRoaqhMnTmj37t0aPHiwqlSpYu0yAQAAHjuEwgAAAA9JuXLlFBYWppiYGJ06dUphYWFaunSpPD095evrq8WLF+v69evWLrPIODk5aceOHapUqZKeffZZJSQkFOt+QUFBOnr0qOLj4/M0Pq/tIx5W64gZM2bojTfe0Ny5czVq1Khi389a0tPTtXLlSgUGBsrZ2Vnjx49XixYt9MMPP+jo0aMaO3asatasae0yAQAAHmuEwgAAAFbg7u6usWPH6tixY4qLi1O9evU0YsQIOTk5KTQ0VDExMcrOzrZ2mYVWsWJFbd26Vd7e3vLx8dHhw4eLbS9vb2/Vq1cvz6eF89I+Ii0tTbt37y7W1hEWi0UTJ07UqFGjtGzZMg0aNKjY9rKW7Oxsbd++XQMGDJCTk5MGDBigChUqKCIiQufOndP06dPVtGnTx/5kNAAAQElBKAwAAGBFJpNJrVu31rx585SUlKTFixcrNTVVXbt2laenp8aMGaNjx45Zu8xCKVOmjNavX68OHTqoQ4cOiouLK7a9goKCtGbNmjyNzUv7iC1btqhMmTJq27ZtUZR3B4vFotGjR2vSpElavXq1QkJCimUfazly5IhGjx4tDw8PdenSRcnJyZo/f75SUlK0bNkyde3aNU+ntQEAAFC0CIUBAABKCAcHB73wwguKiIhQYmKiRo4cqU2bNqlevXpq1aqV5s2bp9TUVGuXWSClSpXS8uXL9eKLL6pz587avHlzsezTu3dv/fDDDzp79uwDx+alfURERIS6du2qUqVKFVWJt+0/ePBgzZ07V5GRkerZs2eR72ENZ8+e1eTJk9WgQQM1atRIO3bs0OjRo5WYmKhNmzYpJCREZcuWtXaZAAAAhkYoDAAAUAI5OTlpxIgROnTokA4dOqQ2bdpo3LhxcnFxUXBwsDZu3KjMzExrl5kvNjY2mjdvnoYNG6bAwECtWLGiyPdo2rSpnnjiCa1bt+6BYx/UPiI7O1tRUVHF0joiMzNToaGhWrFihbZt26bOnTsX+R4PU1pamhYuXChfX195eHjoiy++UFBQkOLj47Vv3z699dZbcnJysnaZAAAA+BdCYQAAgBKuUaNGmjZtmhITE7VmzRqZzWYFBwfL1dVVw4cP18GDB2WxWKxdZp6YTCZ98sknmjRpkkJCQvTFF18U+fq9evXKUwuJB7WP+P7775Wamio/P7+iLFE3b95UcHCwtm7dqm+//VZt2rQp0vUfllu3bmn9+vV64YUX5OzsrHfffVf169fXnj17lJCQoAkTJqhu3brWLhMAAAB3QSgMAADwiLC1tVX37t21cuVKJScna8KECdq3b5+aNGmiRo0aaerUqUpOTrZ2mXkyevRozZ8/X2+++aYmT55cpGv37t1bsbGxunjx4n3HPeikcEREhFq0aFGkJ1yvX7+ugIAAHThwQLGxsXrmmWeKbO2H/YO1AQAAHFFJREFUIScnR7t379bgwYPl7Oysl156SWazWatWrdL58+c1Z84ctW7dmgfGAQAAlHCEwgAAAI+gSpUqafDgwYqLi9OxY8cUGBioWbNmyc3NTd27d9eKFSt08+ZNa5d5XwMHDtQ333yjDz/8UO+++26RnXb28fFRpUqVtHHjxvuOe1BP4cjIyCJtHZGWlqauXbvq5MmT2rVrl7y9vYts7eIWHx+vDz74QE8++aTatWunhIQETZs2TSkpKVqxYoV69OhRLH2XAQAAUDwIhQEAAB5xXl5emjRpkk6ePKlt27apWrVqeu211+Ts7KxBgwYpLi6uxLaX+PPBep9//rkGDRqk7OzsQq9pa2urwMDAB7aQuF/7iDNnzujIkSMKCAgodD2SdOHCBfn6+iotLU27du1SzZo1i2Td4pScnKwZM2aoWbNm8vb2VkREhIYMGaKzZ88qOjpa/fv3l6Ojo7XLBAAAQAEQCgMAADwmzGazfH19tXjxYiUnJ2vWrFlKSEiQj4+PvLy8NHHiRJ06dcraZd6hS5cuio6O1qpVq/Tyyy/r1q1bhV6zd+/e2rZtm65du3bPMfdrHxEZGakaNWqocePGha7l3Llzat++vcxms3bu3KkaNWoUes3icv36dS1btkxdu3aVq6urpk6dqs6dO+vIkSM6dOiQRo0aJVdXV2uXCQAAgEIiFAYAAHgMlStXTqGhoYqJidGpU6f06quvKjw8XDVr1pSvr68WLVp038D0YWvdurV27typ2NhYBQYG6vfffy/Uep07d5adnZ2ioqLuOeZ+7SP+bB1R2N64v/76q9q2bauqVatq+/btqlq1aqHWKw5ZWVmKiopS37595eTkpKFDh8rd3V3bt2/X6dOn9emnn6pBgwbWLhMAAABFiFAYAADgMefu7q4xY8YoPj5e3333nerVq6eRI0fK2dlZr7zyiqKjo4ukbUNhNWzYULt379axY8fUpUsXXb58ucBrOTg4yN/fX2vXrr3nmHu1j7hx44ZiYmIK3Trip59+Utu2beXl5aXNmzerQoUKhVqvKFksFu3bt0/Dhg2Tq6urevbsqRs3bmjp0qVKTk7Wl19+mXu6GQAAAI8f7vIAAAAMwmQyqVWrVpo3b56SkpK0ZMkSpaWlqVu3bvL09MwNjq2pdu3a2r17t9LS0tShQwclJycXeK2goCBFRkYqIyPjrtfv1T5i+/btslgs6tSpU4H3PnDggNq3b69WrVppw4YNKlu2bIHXKkq//vqrJkyYoLp166ply5b6xz/+oQkTJig5OVlr167V888/LwcHB2uXCQAAgGJGKAwAAGBADg4OCg4O1saNG5WYmKiRI0cqKipK3t7eatmypebOnavU1FSr1Obq6qrY2Fg5ODjIx8dHJ0+eLNA6/v7+ysjIUExMzF2v36t9RGRkpHx9fQsc5O7atUsdO3aUv7+/Vq5cKXt7+wKtU1QuXryouXPnqk2bNqpdu7b+/ve/q3///jp58qR27dqlQYMGqXLlylatEQAAAA8XoTAAAIDBOTk5acSIETp48KAOHz4sHx8fTZgwQS4uLgoODtaGDRuUmZn5UGuqUqWKoqOj5eHhIR8fH/3000/5XsPR0VGdO3e+ZwuJu50UtlgsioiIKHDriC1btqhr167q27evFi9efM+excUtPT1dK1asUGBgoFxcXDRx4kS1atVKBw4c0M8//6wxY8bI09PTKrUBAADA+giFAQAAkKthw4aaOnWqzp07pzVr1sjGxkZ9+vSRq6urhg8froMHD8pisTyUWsqXL6/IyEi1aNFC7dq10759+/K9RlBQkNavX3/Xnsl36yl85MgRnTt3Tt27d8/3XmvWrFGPHj301ltvac6cOQ+9H292drZiYmL06quvysnJSa+99poqVaqkyMhInT17VtOmTVOTJk0K/fA8AAAAPPoIhQEAAHAHW1tbde/eXStWrFBSUpImTpyoffv2qUmTJmrYsKH+9re/KSkpqdjrcHBw0KpVqxQYGKhOnTpp+/bt+ZofGBioS5cuac+ePXdcu1v7iMjISD311FP5PkW7bNky9enTR+PHj9enn3760IJXi8WiQ4cO6Z133pG7u7u6du2qCxcu6H//93+VkpKiJUuWqEuXLlY7sQwAAICSiVAYAAAA91WpUiUNGjRIcXFxOnbsmHr16qXZs2fLzc1N/v7+WrFihdLT04ttf1tbWy1cuFADBw6Un5+f1q1bl+e51atXl4+Pz11bSNytfURBWkfMnTtX/fv31/Tp0/X+++/na25BnTlzRp9++qkaNGigZ555RrGxsXrvvfd0/vx5RUZG6uWXXy4xD7cDAABAyUMoDAAAgDzz8vLSxIkTdfLkSUVHR6t69ep67bXX5OLiojfeeEN79uwplvYSZrNZU6dO1V//+lcFBwdryZIleZ4bFBSkNWvW3FHXf7eP+O2337R37958hcKTJ0/W0KFDtXDhQg0dOjTP8woiLS1NCxYsUIcOHeTh4aEFCxYoODhYx48f1/fff6+hQ4eqevXqxVoDAAAAHg+EwgAAAMg3s9ksX19fLV68WCkpKZo9e7ZOnDihtm3b5gbHp06dKtI9TSaTxo4dq5kzZ2rAgAGaOXNmnuYFBQXpzJkzOnjw4G2v/3f7iKioKFWsWFGtWrV64JoWi0Vjx47Vhx9+qBUrVqh///75ei95lZGRobVr1yo4OFhOTk5677339PTTT+u7777TL7/8onHjxqlOnTrFsjcAAAAeX4TCAAAAKJSyZcvqlVdeUXR0tE6dOqVXX31V4eHhqlmzpjp06KBFixbp2rVrRbbfkCFDtHTpUo0aNUofffTRA08me3h4qEmTJne0kPjv9hGRkZHy8/N7YP/dnJwcvfXWW5o2bZrWr1+v4ODggr+Ze6y/a9cuDRo0SM7OzgoJCZGtra1Wr16tpKQkff7552rVqhUPjAMAAECBEQoDAACgyLi7u2vMmDGKj4/X3r17Vb9+fb399ttycnJSv379tG3bNmVnZxd6n759+2rt2rWaMmWKhg0bppycnPuO/7OFxH/6z/YRmZmZ2rx58wNbR2RlZWnAgAFasmSJtmzZIj8/v8K9kf9w9OhRjR07VrVq1VL79u114sQJzZgxQykpKfrmm28UEBAgOzu7ItsPAAAAxkUoDAAAgCJnMpnUsmVLzZ07V0lJSVq6dKmuXr0qPz8/eXp66v3331d8fHyh9ggICNDmzZu1ePFihYWFKTMz855je/furZ9//lnHjx/Pfe0/20fs3r1b169fV9euXe+5xq1bt/TSSy9p48aNiomJUbt27QpVvyQlJSVp2rRpatq0qerXr69NmzZp6NChOnv2rLZt26awsDA5OjoWeh8AAADgPxEKAwAAoFjZ29srODhYGzZs0Pnz5/X2229r8+bN8vb2VsuWLTVnzhxdunSpQGu3b99eO3bs0ObNm9W7d2+lp6ffdZy3t7e8vLxuayHxn+0jIiMj9eyzz6py5cp3nX/jxg317NlTe/bs0c6dO9W8efMC1StJ165d09KlS9WlSxe5ublp5syZ6tKli3788UcdPHhQb7/9tlxdXQu8PgAAAPAgprw8HdpkMlmK4ynSAAAAMK4jR45o6dKlCg8PV2pqqnr06KGwsDD5+fnlu01CfHy8unTpolq1amnDhg13PV373nvvaesP8Qoe+Yl+OJ2qAwnnVcq+tErb2+la4i9qVKO8Jg7spQauFW6bd/XqVQUEBOj06dOKiYlR7dq18/1eMzMztW3bNoWHh2vdunUqVaqU+vTpo379+snHx0dmM2c1AAAAUDRMJpMsFst9H0BBKAwAAACrysrK0tatW7VkyRKtX79e5cuXV0hIiMLCwvTMM8/k+YFqZ86c0XPPPady5cpp8+bNqlatWu61/adSNerv+3T6arZszSZl5dx5b2trlrJypPoujpoQ+JSaeVbWpUuX1K1bN129elXR0dF64okn8vy+LBaL9u3bp/DwcK1YsUJXrlxR9+7d1a9fP/n7+8vBwSHPawEAAAB5RSgMAACAR8rly5e1atUqLVmyRHFxcXr66acVGhqqfv36ycXF5YHzL1y4oG7duunGjRvatm2bXGq46pPN8Vq4+6TMJukuWfAd/hz3UuPq2jjpNdnZmLVlyxY5OTnl6T0kJCRo+fLlCg8PV0JCgtq1a6e+ffsqODj4nu0pAAAAgKJCKAwAAIBH1i+//KKlS5dq6dKlOnfunLp06aKwsDD17NlTpUuXvue8K1euqEePHjp1+ox83lusfedu3PVk8APlZMv+0i/aPflVVaty/zD3t99+08qVKxUeHq69e/fK29tbr7zyikJCQuTh4ZH/vQEAAIACIhQGAADAIykzMzO3r3BOTo5iY2O1ZMkSrVq1Sra2turTp49CQ0P17LPP3rW9RHp6unyGTtPFKk/LZLbN196W7CxJFpls7GRrNql/G0990L3+HeNu3LihDRs2KDw8XFu2bFHVqlUVEhKifv36qXHjxrfVZbFYdPPmzfuG2QAAAEBRIBQGAADAI+fatWt66qmn9Nlnn+nFF1/MDVN37typpk2bas2aNVq0aJF27NihmjVrKiwsTKGhofL09JT0R6Dc0qeDzlZrqbINOill5Ucq+1QHlXvKV2m7v1bW5SRV7fH2PffPSE5Q8uIRcgr5RA7uT8skadWg1mrmWVnZ2dn69ttvFR4erlWrVunGjRsqW7asypUrd98ewTk5OSpVqpQSEhKK+NMCAAAAbpeXUDh/xyYAAACAYjZ69GiVLVtWAQEBkiR7e3tJUsWKFVW2bFllZ2frn//8p9avX69Zs2Zp3Lhx+uijj2RjYyNJev/993XmaqZsavwxz2xnL5PNH6eOTXb2ks39b4HN9mUlWWRb0fmPOSbpnRX71fS3bfr6669z+xbPnDlTgwcP1vHjxxURESF7e/vcGv7brVu3VLt27UJ/NgAAAEBRMFu7AAAAAOBPCxYs0Pz589W3b1+VLVtWknLbMGzbtk2tW7fWggULtHjxYgUEBCgqKkpZWVl67bXX5OXlpfLly2vG0jX6/WaWcu66g0km3ffQRC6zfRlJfzx07mRalr49lKAxY8YoKSlJERERCg4O/uN6To4yMzOVkZGhmzdvauXKlfr888918+bN3F/p6enKzMws7McDAAAAFAlOCgMAAKBEmDJlij799FM9/fTTua+dOXNGf//73yVJkZGR+uSTT9ShQ4fc67a2f9zOurm5qU2bNpozZ44GzdusVXM/0eXtC3V5+1dSTpYykn754+8tOSpTp1Xu/Jybv0tms0x2Dv/uAZyTJelfp4r/xUbZCnhzrF5s56lq1ardVrebm5s8PDz03nvvSZLOnz8vGxsbzZgxI3dMhw4dNGTIkCL4lAAAAIDCIxQGAACA1V24cEGrVq3S2rVrNX36dEnSgQMH1L17d4WGhkqSZs2adVtgvG/fPnl7e+vSpUu6du2a0tPTlZSUpMtmR1V7/sM87XspaqZuHIu767UzU3re9s8TJM2pUkUXL168Y+zNmzdlNps1ZcqUO65t2bJF586dy1M9AAAAwMNAKAwAAACrq169uvbv3y9JuaFw06ZNdf78eZnNZn322WdKSEjIDYVPnz6tNm3a6KuvvtL48eN19epVZWVlae/evbrp7a+UnV9L5rvf6lqybqnGwHmyKV1elZ97U5U6vyGT6d+9gG/8ul+pUbNUvc94lapeK/f1cnYWRf7l2buuaWNjo8uXLys6OvqOaz///LMqVapUsA8GAAAAKAaEwgAAAChRbt26ldt/12z+4xEYfn5+6t27tywWS+645s2b64UXXlBoaKjGjRunc+fOacGCBar7QYRs6vnK/K/2D+cXDlGVbkNl71pPkpRzK10mOwdJkk25O8PazJQTktlG6b/+oNK1mua+brEzq1atWneMlySLxaK0tDTt2LHjjmsXL15Uy5YtC/BJAAAAAMWDUBgAAAAlipeXl1xcXCRJP/74o+bPn69Nmzbleb69nZ0ysv/oD2zJyVZWWopKOf07zDWXKn3PuTk3r+v3n3eoit9bSt02X+WbdJddFbc/1rW1uW1senq6SpUqJUnKysqSq6ur/vKXv9yxZmxsrK5cuZLn+gEAAIDiRigMAAAAq8vOztatW7fk4OBw2wPaLl++rI0bN2rOnDm3jbdYLMrIyFCpUqVyTxP/qXb1clo/sptkY6ucm7/LXKq0zs58WSYbO8lsI7Odg1zfXHjXOtJil8muqrvKNeikzN9O6dKmGXLqN0Umk1m1q5e7bWxKSooqVKggSfL29la/fv00efJk2dnZqU+fPrnjatWqpTp16hTq8wEAAACKEqEwAAAArO7gwYNq3rz5Pa+bTKa7vv7LL7+odu3akqQzZ85o6NChMtvVkMnOXk4vTVLq1vlybP2Cfv/nt3JwbyC7Km66GDnjrmtd/zFG13+MkUv/P3oaV2jbV0mLhyt16zw5+Q1RM4/bW00cOnRIXl5ekiQPDw/99ttvio+PV40aNfTNN9/kjitXrpxWr16d9w8DAAAAKGbmBw8BAAAAilejRo2UnJysy5cv3/YrKipKTzzxxB2vX7p0SefPn1dGRoYGDRqkOXPmKCMjQw0aNNCI10PytbfFkqMrcSt1KWq2qnT7i+yqPCFJMts5qHrvsfr951glr/+bOte5PRRes2aN2rVrp9mzZ+vJJ59UWlqa2rRpo+eee05xcXEaNWqUbt26penTp8vV1bXIPisAAACgsDgpDAAAAKuzs7OTk5PTHa+XKVNGklSxYsW7zjOZTLJYLNq1a5fq1auX+7qt2aw/DhdbJEvOv3/p36eOLTnZSk/Yr7Tdy5V1OUlVe7ytst5tb6+ryhNyfnGCflv1kUJ7+GrWrFl67rnnFBsbq6ioKE2dOlXZ2dlq3ry5HB0ddfjwYb3zzjuqWrWq7OzsNH78eJnNZiUkJMhkMqlatWpydHQsgk8MAAAAKDhOCgMAAKDEysjIUEZGxj2vOzs764svvrgtEJYke2XKYpHsqrjLZOegUjW8ZC5TQemnDqp03TaSpKwrF3Rp00yZ7cvKZcCsOwLh3LVq1NWqqB1ydHTUp59+qqysLDVs2FCzZ89WnTp1NH/+fD377LPq0aOH1q9frwkTJujIkSOaNWuW4uLiFBoaqsaNG6tFixbKzs4uug8HAAAAKCCTxWJ58CCTyZKXcQAAAEBJMTHyZy2JO6WsnHvfx2Zduyjb8lXved3WbFL/Np76oHt95eTk6Nq1a7kPl8tdIytL2dnZsre3v2892dnZsrGxyd+bAAAAAPLpXz9Nd/eHcvw5hlAYAAAAj6Os7Bz9v6//oe3xF+4bDN+LrdmkTvWqa05IE9na8AN2AAAAeDTkJRTm7hYAAACPJVsbs+aGNFFYG09Jkvm+t8X/ZjZJJkn923gSCAMAAOCxxElhAAAAPPb2n0rVRxt+0s9JV2VnY1Jm9p33tn++Xt/FURMCn1Izz8pWqBQAAAAoHNpHAAAAAP/hx8QrijhyXj+cvqyEC9eVkZUte1sb1a5eTs08KimgYQ01cK3w4IUAAACAEopQGAAAAAAAAAAMhJ7CAAAAAAAAAIDbEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICBEAoDAAAAAAAAgIEQCgMAAAAAAACAgRAKAwAAAAAAAICB2OZ1oMlkKs46AAAAAAAAAAAPgclisVi7BgAAAAAAAADAQ0L7CAAAAAAAAAAwEEJhAAAAAAAAADAQQmEAAAAAAAAAMBBCYQAAAAAAAAAwEEJhAAAAAAAAADAQQmEAAAAAAAAAMBBCYQAAAAAAAAAwkP8PTEEEt+SuGLQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x1350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import utils\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "# plt.rcParams[\"figure.dpi\"] = 90\n",
    "plt.rcParams[\"font.size\"] = 10\n",
    "seqs_normal, adj, node_f, nks, conns, t_map = utils.init_data('都市名园')\n",
    "df = pd.DataFrame(conns)\n",
    "df.columns = ['s','t','w']\n",
    "\n",
    "G = nx.from_pandas_edgelist(df, source='s', target='t', edge_attr='w')\n",
    "\n",
    "lengths = [i['w'] for i in dict(G.edges).values()]\n",
    "labels = {i:i for i in dict(G.nodes).keys()}/+\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "pos = nx.spring_layout(G)\n",
    "nx.draw_networkx_nodes(G, pos, ax = ax, labels=True)\n",
    "edge_labels = {e: G.edges[e]['w'] for e in G.edges}\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)\n",
    "nx.draw_networkx_edges(G, pos, lengths=lengths,ax=ax)\n",
    "_ = nx.draw_networkx_labels(G, pos, labels, ax=ax)\n",
    "plt.margins(0.2, tight=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=%3C%21DOCTYPE%20html%3E%0A%3Chead%3E%20%20%20%20%0A%20%20%20%20%3Cmeta%20http-equiv%3D%22content-type%22%20content%3D%22text/html%3B%20charset%3DUTF-8%22%20/%3E%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%3Cscript%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20L_NO_TOUCH%20%3D%20false%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20L_DISABLE_3D%20%3D%20false%3B%0A%20%20%20%20%20%20%20%20%3C/script%3E%0A%20%20%20%20%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//cdn.jsdelivr.net/npm/leaflet%401.6.0/dist/leaflet.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//code.jquery.com/jquery-1.12.4.min.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js%22%3E%3C/script%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//cdn.jsdelivr.net/npm/leaflet%401.6.0/dist/leaflet.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css%22/%3E%0A%20%20%20%20%3Cstyle%3Ehtml%2C%20body%20%7Bwidth%3A%20100%25%3Bheight%3A%20100%25%3Bmargin%3A%200%3Bpadding%3A%200%3B%7D%3C/style%3E%0A%20%20%20%20%3Cstyle%3E%23map%20%7Bposition%3Aabsolute%3Btop%3A0%3Bbottom%3A0%3Bright%3A0%3Bleft%3A0%3B%7D%3C/style%3E%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cmeta%20name%3D%22viewport%22%20content%3D%22width%3Ddevice-width%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20initial-scale%3D1.0%2C%20maximum-scale%3D1.0%2C%20user-scalable%3Dno%22%20/%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cstyle%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23map_3017080d514b4b9e871314c95deb202e%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20width%3A%20100.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20height%3A%20100.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20left%3A%200.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20top%3A%200.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C/style%3E%0A%20%20%20%20%20%20%20%20%0A%3C/head%3E%0A%3Cbody%3E%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22folium-map%22%20id%3D%22map_3017080d514b4b9e871314c95deb202e%22%20%3E%3C/div%3E%0A%20%20%20%20%20%20%20%20%0A%3C/body%3E%0A%3Cscript%3E%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20map_3017080d514b4b9e871314c95deb202e%20%3D%20L.map%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%22map_3017080d514b4b9e871314c95deb202e%22%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20center%3A%20%5B22.575367%2C%20114.10372%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20crs%3A%20L.CRS.EPSG3857%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20zoom%3A%2016%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20zoomControl%3A%20true%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20preferCanvas%3A%20false%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29%3B%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20tile_layer_81c4879b42d94dc19133b3bcf9cd3125%20%3D%20L.tileLayer%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%22https%3A//%7Bs%7D.tile.openstreetmap.org/%7Bz%7D/%7Bx%7D/%7By%7D.png%22%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22attribution%22%3A%20%22Data%20by%20%5Cu0026copy%3B%20%5Cu003ca%20href%3D%5C%22http%3A//openstreetmap.org%5C%22%5Cu003eOpenStreetMap%5Cu003c/a%5Cu003e%2C%20under%20%5Cu003ca%20href%3D%5C%22http%3A//www.openstreetmap.org/copyright%5C%22%5Cu003eODbL%5Cu003c/a%5Cu003e.%22%2C%20%22detectRetina%22%3A%20false%2C%20%22maxNativeZoom%22%3A%2018%2C%20%22maxZoom%22%3A%2018%2C%20%22minZoom%22%3A%200%2C%20%22noWrap%22%3A%20false%2C%20%22opacity%22%3A%201%2C%20%22subdomains%22%3A%20%22abc%22%2C%20%22tms%22%3A%20false%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28map_3017080d514b4b9e871314c95deb202e%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20feature_group_3a1553bc1dca4dfeb09aff8f8d5f7d49%20%3D%20L.featureGroup%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28map_3017080d514b4b9e871314c95deb202e%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_fcdbe824ac7e4d7aa6d13500846f5ba6%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.575367%2C%20114.10372%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22red%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_3a1553bc1dca4dfeb09aff8f8d5f7d49%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_bf613ed5320043a4b25e49d692d27575%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_c1534f92e0d1417e8c999061c1268322%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_c1534f92e0d1417e8c999061c1268322%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E4%B8%9C%E7%BF%A0%E8%8A%B1%E5%9B%AD%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_bf613ed5320043a4b25e49d692d27575.setContent%28html_c1534f92e0d1417e8c999061c1268322%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_fcdbe824ac7e4d7aa6d13500846f5ba6.bindPopup%28popup_bf613ed5320043a4b25e49d692d27575%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_c12a7ef908fc4b6185dc0570b84d4bfb%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.57604%2C%20114.10486999999999%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_3a1553bc1dca4dfeb09aff8f8d5f7d49%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_0e602ad51a9e4295a41b68427b1589ff%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_d0808b3da1b84acdb396d9504ddb155d%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_d0808b3da1b84acdb396d9504ddb155d%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E6%AD%A6%E8%AD%A6%E7%94%9F%E6%B4%BB%E5%8C%BA%E9%93%B6%E9%BE%99%E8%8A%B1%E5%9B%AD%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_0e602ad51a9e4295a41b68427b1589ff.setContent%28html_d0808b3da1b84acdb396d9504ddb155d%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_c12a7ef908fc4b6185dc0570b84d4bfb.bindPopup%28popup_0e602ad51a9e4295a41b68427b1589ff%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%3C/script%3E onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x2bfc813af10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('合正星园', '宝琳珠宝交易中心'): 0.260241298753267,\n",
       " ('合正星园', '湖景大厦'): 0.22962508076107815,\n",
       " ('宝琳珠宝交易中心', '湖景大厦'): 0.1573976435026092}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b485d3cd60>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_.seqs[1][0:96*7].plot(figsize=(20,10))\n",
    "train_.seqs_noised[1][0:96*7].plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "from tqdm import tqdm\n",
    "\n",
    "basic_info_df = pd.read_csv('generated/data/parkings_info.csv')\n",
    "basic_info_df['lat_long'] = list(zip(basic_info_df['latitude'], basic_info_df['longitude']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image, display\n",
    "from datetime import date, timedelta\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def build_area_seqs(target_area, start='2016-08-01', end='2017-01-01'):\n",
    "    # 整合到一个文件中\n",
    "    area_df = pd.DataFrame()\n",
    "    for name in target_area.parking_name:\n",
    "        file_name = 'generated/data/seqs/'+name+'_seq.csv'\n",
    "        file_df = pd.read_csv(file_name)\n",
    "        file_df['parking'] = nks[name]\n",
    "        cols = file_df.columns.tolist()\n",
    "        cols = [cols[0], cols[2], cols[1]]\n",
    "        file_df = file_df[cols]\n",
    "        if len(area_df)>0:\n",
    "            area_df = pd.concat([area_df, file_df])\n",
    "        else:\n",
    "            area_df = file_df\n",
    "\n",
    "    out_bound_indexes = area_df[(area_df['date'] < start) | (area_df['date'] >= end)].index \n",
    "    area_df.drop(out_bound_indexes, inplace = True) \n",
    "    return area_df.pivot_table('occupy', ['date'], 'parking')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graph_utils import build_graph\n",
    "\n",
    "target_park = '电影大厦'\n",
    "target_area, adj, target_map, nks, kns = build_graph(basic_info_df, target_park)\n",
    "target_park_basic_info = basic_info_df.loc[basic_info_df.parking_name == target_park].iloc[0]\n",
    "\n",
    "seqs_raw = build_area_seqs(target_area, start='2016-10-01', end='2016-11-01')\n",
    "# normalization\n",
    "seqs_normal = seqs_raw/seqs_raw.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=%3C%21DOCTYPE%20html%3E%0A%3Chead%3E%20%20%20%20%0A%20%20%20%20%3Cmeta%20http-equiv%3D%22content-type%22%20content%3D%22text/html%3B%20charset%3DUTF-8%22%20/%3E%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%3Cscript%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20L_NO_TOUCH%20%3D%20false%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20L_DISABLE_3D%20%3D%20false%3B%0A%20%20%20%20%20%20%20%20%3C/script%3E%0A%20%20%20%20%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//cdn.jsdelivr.net/npm/leaflet%401.6.0/dist/leaflet.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//code.jquery.com/jquery-1.12.4.min.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js%22%3E%3C/script%3E%0A%20%20%20%20%3Cscript%20src%3D%22https%3A//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js%22%3E%3C/script%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//cdn.jsdelivr.net/npm/leaflet%401.6.0/dist/leaflet.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css%22/%3E%0A%20%20%20%20%3Clink%20rel%3D%22stylesheet%22%20href%3D%22https%3A//rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css%22/%3E%0A%20%20%20%20%3Cstyle%3Ehtml%2C%20body%20%7Bwidth%3A%20100%25%3Bheight%3A%20100%25%3Bmargin%3A%200%3Bpadding%3A%200%3B%7D%3C/style%3E%0A%20%20%20%20%3Cstyle%3E%23map%20%7Bposition%3Aabsolute%3Btop%3A0%3Bbottom%3A0%3Bright%3A0%3Bleft%3A0%3B%7D%3C/style%3E%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cmeta%20name%3D%22viewport%22%20content%3D%22width%3Ddevice-width%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20initial-scale%3D1.0%2C%20maximum-scale%3D1.0%2C%20user-scalable%3Dno%22%20/%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cstyle%3E%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23map_a88aa4e961ec4a1699981fefde7a6ec8%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20position%3A%20relative%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20width%3A%20100.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20height%3A%20100.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20left%3A%200.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20top%3A%200.0%25%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%3C/style%3E%0A%20%20%20%20%20%20%20%20%0A%3C/head%3E%0A%3Cbody%3E%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20%3Cdiv%20class%3D%22folium-map%22%20id%3D%22map_a88aa4e961ec4a1699981fefde7a6ec8%22%20%3E%3C/div%3E%0A%20%20%20%20%20%20%20%20%0A%3C/body%3E%0A%3Cscript%3E%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20map_a88aa4e961ec4a1699981fefde7a6ec8%20%3D%20L.map%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%22map_a88aa4e961ec4a1699981fefde7a6ec8%22%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20center%3A%20%5B22.551416%2C%20114.12001299999999%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20crs%3A%20L.CRS.EPSG3857%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20zoom%3A%2016%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20zoomControl%3A%20true%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20preferCanvas%3A%20false%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29%3B%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20tile_layer_f8f45613c924404a98e6fa6eebccf95f%20%3D%20L.tileLayer%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%22https%3A//%7Bs%7D.tile.openstreetmap.org/%7Bz%7D/%7Bx%7D/%7By%7D.png%22%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22attribution%22%3A%20%22Data%20by%20%5Cu0026copy%3B%20%5Cu003ca%20href%3D%5C%22http%3A//openstreetmap.org%5C%22%5Cu003eOpenStreetMap%5Cu003c/a%5Cu003e%2C%20under%20%5Cu003ca%20href%3D%5C%22http%3A//www.openstreetmap.org/copyright%5C%22%5Cu003eODbL%5Cu003c/a%5Cu003e.%22%2C%20%22detectRetina%22%3A%20false%2C%20%22maxNativeZoom%22%3A%2018%2C%20%22maxZoom%22%3A%2018%2C%20%22minZoom%22%3A%200%2C%20%22noWrap%22%3A%20false%2C%20%22opacity%22%3A%201%2C%20%22subdomains%22%3A%20%22abc%22%2C%20%22tms%22%3A%20false%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28map_a88aa4e961ec4a1699981fefde7a6ec8%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20feature_group_d1abc17d7106427e8c2d5c4642a3415a%20%3D%20L.featureGroup%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28map_a88aa4e961ec4a1699981fefde7a6ec8%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_e9e108fb9b3e4947a374301cc9ddd220%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.552334%2C%20114.12359599999999%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_eb334f11e0b84c0a82619ad8ecd9f1fb%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_7bbeffe27fc54526adcf2c9d570664f8%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_7bbeffe27fc54526adcf2c9d570664f8%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E5%8D%8A%E5%B2%9B%E5%A4%A7%E5%8E%A6%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_eb334f11e0b84c0a82619ad8ecd9f1fb.setContent%28html_7bbeffe27fc54526adcf2c9d570664f8%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_e9e108fb9b3e4947a374301cc9ddd220.bindPopup%28popup_eb334f11e0b84c0a82619ad8ecd9f1fb%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_45f3e65855224c48a83a43d6a43d42dc%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.552334%2C%20114.12359599999999%5D%2C%20%5B22.54983%2C%20114.123911%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_643f6f13e06a405087faa4868fda6f25%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.552334%2C%20114.12359599999999%5D%2C%20%5B22.553094%2C%20114.12258600000001%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_3c202abbedb3444b84739409620cfccc%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.552334%2C%20114.12359599999999%5D%2C%20%5B22.550572%2C%20114.123378%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_3051c2f2c83e4090acfaa2e2def5c9a6%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.550429%2C%20114.11701599999999%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_55b024f73ca9467ab31932402528c528%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_aa3cce37f65f49ab88393c281ba21183%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_aa3cce37f65f49ab88393c281ba21183%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E5%8D%8E%E5%AE%89%E5%9B%BD%E9%99%85%E5%A4%A7%E9%85%92%E5%BA%97%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_55b024f73ca9467ab31932402528c528.setContent%28html_aa3cce37f65f49ab88393c281ba21183%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_3051c2f2c83e4090acfaa2e2def5c9a6.bindPopup%28popup_55b024f73ca9467ab31932402528c528%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_aa480ec17cea4f10a38ddd278aedd0de%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.550429%2C%20114.11701599999999%5D%2C%20%5B22.553417%2C%20114.11695%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_a9b34701d9794a7287169fc55f89dbd7%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.550429%2C%20114.11701599999999%5D%2C%20%5B22.551051%2C%20114.11769%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_3970e0a78d6e4d1cb2f22b6107137c35%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.550429%2C%20114.11701599999999%5D%2C%20%5B22.551416%2C%20114.12001299999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_4defe31064a945c59f040344f02e834d%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.550429%2C%20114.11701599999999%5D%2C%20%5B22.551482999999998%2C%20114.119224%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_38e2962d104f413ab4975ac6ef61f610%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.54983%2C%20114.123911%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_8727ed1cec19406f8f437c7d66f14e75%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_5ed9867f7a7b435296a1913baf1f4a37%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_5ed9867f7a7b435296a1913baf1f4a37%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E5%90%88%E4%BD%9C%E9%87%91%E8%9E%8D%E5%A4%A7%E5%8E%A6%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_8727ed1cec19406f8f437c7d66f14e75.setContent%28html_5ed9867f7a7b435296a1913baf1f4a37%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_38e2962d104f413ab4975ac6ef61f610.bindPopup%28popup_8727ed1cec19406f8f437c7d66f14e75%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_fc70e18db0b943558784d24ccd8c424e%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.54983%2C%20114.123911%5D%2C%20%5B22.552334%2C%20114.12359599999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_c874563314054505bf257cf02dad0fe9%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.54983%2C%20114.123911%5D%2C%20%5B22.550572%2C%20114.123378%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_568ba69830944fe39f2dbfc3d79dfcc2%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.554795000000002%2C%20114.11768500000001%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_24cfe321038c4a3f94e1de2a5dd09f11%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_47e29e1845a04bf8b70892e4c8de3bfc%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_47e29e1845a04bf8b70892e4c8de3bfc%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E5%9B%BD%E9%83%BD%E8%8A%B1%E5%9B%AD%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_24cfe321038c4a3f94e1de2a5dd09f11.setContent%28html_47e29e1845a04bf8b70892e4c8de3bfc%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_568ba69830944fe39f2dbfc3d79dfcc2.bindPopup%28popup_24cfe321038c4a3f94e1de2a5dd09f11%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_61e97f0d5ba0417b97b6607cc3925e3b%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.554795000000002%2C%20114.11768500000001%5D%2C%20%5B22.553417%2C%20114.11695%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_aca4dae7d9a6444d9c54aaea667f8db8%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.554795000000002%2C%20114.11768500000001%5D%2C%20%5B22.553402%2C%20114.119239%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_9e1693094b974015b795f1c2e164f6ca%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.553417%2C%20114.11695%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_d6c4a1cfc4704c5a9885ade8b6752474%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_5126ae7361d04a618263980d3d9fef88%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_5126ae7361d04a618263980d3d9fef88%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E5%AE%9D%E4%B8%BD%E5%A4%A7%E5%8E%A6%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_d6c4a1cfc4704c5a9885ade8b6752474.setContent%28html_5126ae7361d04a618263980d3d9fef88%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_9e1693094b974015b795f1c2e164f6ca.bindPopup%28popup_d6c4a1cfc4704c5a9885ade8b6752474%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_dec396906e884385a8ebc81c6f77e287%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553417%2C%20114.11695%5D%2C%20%5B22.550429%2C%20114.11701599999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_6cf6c3c3de4747729e631d7f9f38b614%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553417%2C%20114.11695%5D%2C%20%5B22.554795000000002%2C%20114.11768500000001%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_4ca2c640461d4ef7a9b6146c203b7e5a%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553417%2C%20114.11695%5D%2C%20%5B22.551051%2C%20114.11769%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_e82a885deae943d585d43230258d5d9b%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553417%2C%20114.11695%5D%2C%20%5B22.553402%2C%20114.119239%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_9cc79b01fe4446439141fe36298ffdd4%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553417%2C%20114.11695%5D%2C%20%5B22.551482999999998%2C%20114.119224%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_63c21557284c4d63bef3dfe3a00b1669%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.553094%2C%20114.12258600000001%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_d65df2c3570c41e1a193ffd2f7702e01%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_7939a40556654fd29f079fdf61fdd18a%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_7939a40556654fd29f079fdf61fdd18a%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E5%B7%A5%E4%BA%BA%E6%96%87%E5%8C%96%E5%AE%AB%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_d65df2c3570c41e1a193ffd2f7702e01.setContent%28html_7939a40556654fd29f079fdf61fdd18a%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_63c21557284c4d63bef3dfe3a00b1669.bindPopup%28popup_d65df2c3570c41e1a193ffd2f7702e01%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_372c2d6f761241f4a3f714b746da4302%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553094%2C%20114.12258600000001%5D%2C%20%5B22.552334%2C%20114.12359599999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_c46d55fd735e46cbadf30c164e4d2879%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553094%2C%20114.12258600000001%5D%2C%20%5B22.553402%2C%20114.119239%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_a8abf42bd1324fb4a37122fcbada36b9%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553094%2C%20114.12258600000001%5D%2C%20%5B22.550572%2C%20114.123378%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_753ccbf3d0974931a24a81d8daf196cb%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553094%2C%20114.12258600000001%5D%2C%20%5B22.551416%2C%20114.12001299999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_ce6b374069c54dd6b5d5946853de34ce%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.551051%2C%20114.11769%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_2f1f90d2081b41e8a91bf621fae2a47c%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_9a4225cfd281491eb498e5e2d69edcf1%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_9a4225cfd281491eb498e5e2d69edcf1%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E6%8C%AF%E4%B8%9A%E5%A4%A7%E5%8E%A6%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_2f1f90d2081b41e8a91bf621fae2a47c.setContent%28html_9a4225cfd281491eb498e5e2d69edcf1%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_ce6b374069c54dd6b5d5946853de34ce.bindPopup%28popup_2f1f90d2081b41e8a91bf621fae2a47c%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_4e03ffb6e8804d11a2616dfd0466c245%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551051%2C%20114.11769%5D%2C%20%5B22.550429%2C%20114.11701599999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_92f75a1d76154a80829beac3e0599191%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551051%2C%20114.11769%5D%2C%20%5B22.553417%2C%20114.11695%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_6e131b3c05af4cd9804fe3a94b7e89ff%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551051%2C%20114.11769%5D%2C%20%5B22.553402%2C%20114.119239%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_ad077d739e0e4f4ba178a3fcd33590ba%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551051%2C%20114.11769%5D%2C%20%5B22.551416%2C%20114.12001299999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_4fe7802ac7f644f183528b00419e35b7%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551051%2C%20114.11769%5D%2C%20%5B22.551482999999998%2C%20114.119224%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_9b45acfe20404c6190944b6b72c18c26%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.553402%2C%20114.119239%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_730d74ba72b146a6bb89be76b653874b%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_3cd68f6095e44ad796577724e395fe5c%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_3cd68f6095e44ad796577724e395fe5c%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E6%A1%82%E8%8A%B1%E5%A4%A7%E5%8E%A6%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_730d74ba72b146a6bb89be76b653874b.setContent%28html_3cd68f6095e44ad796577724e395fe5c%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_9b45acfe20404c6190944b6b72c18c26.bindPopup%28popup_730d74ba72b146a6bb89be76b653874b%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_99e88be88a4541619212239cec8437b4%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553402%2C%20114.119239%5D%2C%20%5B22.554795000000002%2C%20114.11768500000001%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_40f97d1759a0403e8847cafc3e1ab21b%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553402%2C%20114.119239%5D%2C%20%5B22.553417%2C%20114.11695%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_eba632e87d8d49bb98d0cbacb548ab29%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553402%2C%20114.119239%5D%2C%20%5B22.553094%2C%20114.12258600000001%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_584c3e8a9d794c428f01ff84fc240aba%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553402%2C%20114.119239%5D%2C%20%5B22.551051%2C%20114.11769%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_470a019d77b24dfaad0bfbb75c904bb7%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553402%2C%20114.119239%5D%2C%20%5B22.551416%2C%20114.12001299999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_cae7c45e5c2045e09be4a1e096785a00%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.553402%2C%20114.119239%5D%2C%20%5B22.551482999999998%2C%20114.119224%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_bef06f33a9de44469b233a23150afeea%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.550572%2C%20114.123378%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_d89eef93417f403daf1a3d074c68c3cc%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_f517b623f6364edf8197fdc3294eeb8d%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_f517b623f6364edf8197fdc3294eeb8d%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E6%B0%B8%E6%96%B0%E5%95%86%E4%B8%9A%E5%9F%8E%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_d89eef93417f403daf1a3d074c68c3cc.setContent%28html_f517b623f6364edf8197fdc3294eeb8d%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_bef06f33a9de44469b233a23150afeea.bindPopup%28popup_d89eef93417f403daf1a3d074c68c3cc%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_66c5aba7dadf4009bf6e3d2a0ad6b229%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.550572%2C%20114.123378%5D%2C%20%5B22.552334%2C%20114.12359599999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_d5e8792f393e48828196147bb1a17ba8%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.550572%2C%20114.123378%5D%2C%20%5B22.54983%2C%20114.123911%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_fd0f68ed41c94f648ced7d2ebbc69c50%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.550572%2C%20114.123378%5D%2C%20%5B22.553094%2C%20114.12258600000001%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_ae7c9790222a4544821925711bf074eb%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.550572%2C%20114.123378%5D%2C%20%5B22.551416%2C%20114.12001299999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_89e5dadce9af47babf8b5fbcf92693a1%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.551416%2C%20114.12001299999999%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22red%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_a92905ec9f9e4678bb943943bc181c53%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_7266512e8ad644528161ba08ef3fe0eb%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_7266512e8ad644528161ba08ef3fe0eb%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E7%94%B5%E5%BD%B1%E5%A4%A7%E5%8E%A6%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_a92905ec9f9e4678bb943943bc181c53.setContent%28html_7266512e8ad644528161ba08ef3fe0eb%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_89e5dadce9af47babf8b5fbcf92693a1.bindPopup%28popup_a92905ec9f9e4678bb943943bc181c53%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_a6a400608b2f4340a2cfad8bf6d16279%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551416%2C%20114.12001299999999%5D%2C%20%5B22.550429%2C%20114.11701599999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_75667f0370ee4d6f89362bf343394148%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551416%2C%20114.12001299999999%5D%2C%20%5B22.553094%2C%20114.12258600000001%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_590c2fe1727340548d22436637bf9efe%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551416%2C%20114.12001299999999%5D%2C%20%5B22.551051%2C%20114.11769%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_69c325a17a924a9db90614585f076795%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551416%2C%20114.12001299999999%5D%2C%20%5B22.553402%2C%20114.119239%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_30c3da3369ef455399a72ef77405ae15%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551416%2C%20114.12001299999999%5D%2C%20%5B22.550572%2C%20114.123378%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_9c207e9d6ad24ee9b46e5807971a24d3%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551416%2C%20114.12001299999999%5D%2C%20%5B22.551482999999998%2C%20114.119224%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20circle_marker_e9a428c950eb442d997ccae59268f841%20%3D%20L.circleMarker%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B22.551482999999998%2C%20114.119224%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22yellow%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20true%2C%20%22fillColor%22%3A%20%22green%22%2C%20%22fillOpacity%22%3A%200.6%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22opacity%22%3A%201.0%2C%20%22radius%22%3A%205%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%203%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20var%20popup_1b80e74eaf884a929b48cdea7751fe74%20%3D%20L.popup%28%7B%22maxWidth%22%3A%20%22100%25%22%7D%29%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20html_f826473f22ce4b86933b5e8aaa4b444c%20%3D%20%24%28%60%3Cdiv%20id%3D%22html_f826473f22ce4b86933b5e8aaa4b444c%22%20style%3D%22width%3A%20100.0%25%3B%20height%3A%20100.0%25%3B%22%3E%E7%BA%A2%E5%9B%B4%E5%9D%8A%E5%81%9C%E8%BD%A6%E5%9C%BA%3C/div%3E%60%29%5B0%5D%3B%0A%20%20%20%20%20%20%20%20%20%20%20%20popup_1b80e74eaf884a929b48cdea7751fe74.setContent%28html_f826473f22ce4b86933b5e8aaa4b444c%29%3B%0A%20%20%20%20%20%20%20%20%0A%0A%20%20%20%20%20%20%20%20circle_marker_e9a428c950eb442d997ccae59268f841.bindPopup%28popup_1b80e74eaf884a929b48cdea7751fe74%29%0A%20%20%20%20%20%20%20%20%3B%0A%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_84aba4701d974d8cbc3506e3d44fd4af%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551482999999998%2C%20114.119224%5D%2C%20%5B22.550429%2C%20114.11701599999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_c76ed3cbe1514e0599e875be237e0bd0%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551482999999998%2C%20114.119224%5D%2C%20%5B22.553417%2C%20114.11695%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_980ac2573c0b4e74926202c9cd4fe507%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551482999999998%2C%20114.119224%5D%2C%20%5B22.551051%2C%20114.11769%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_9da23e301a284d5fbea1cf3fa9d58195%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551482999999998%2C%20114.119224%5D%2C%20%5B22.553402%2C%20114.119239%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%20%20%20%20%0A%20%20%20%20%20%20%20%20%20%20%20%20var%20poly_line_45c0a619e7474438bdae6cdfdd4e220b%20%3D%20L.polyline%28%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%5B%5B22.551482999999998%2C%20114.119224%5D%2C%20%5B22.551416%2C%20114.12001299999999%5D%5D%2C%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%7B%22bubblingMouseEvents%22%3A%20true%2C%20%22color%22%3A%20%22blue%22%2C%20%22dashArray%22%3A%20null%2C%20%22dashOffset%22%3A%20null%2C%20%22fill%22%3A%20false%2C%20%22fillColor%22%3A%20%22blue%22%2C%20%22fillOpacity%22%3A%200.2%2C%20%22fillRule%22%3A%20%22evenodd%22%2C%20%22lineCap%22%3A%20%22round%22%2C%20%22lineJoin%22%3A%20%22round%22%2C%20%22noClip%22%3A%20false%2C%20%22opacity%22%3A%200.2%2C%20%22smoothFactor%22%3A%201.0%2C%20%22stroke%22%3A%20true%2C%20%22weight%22%3A%201.5%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20%29.addTo%28feature_group_d1abc17d7106427e8c2d5c4642a3415a%29%3B%0A%20%20%20%20%20%20%20%20%0A%3C/script%3E onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1d89d2f0a00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_nodes_features(area_df):\n",
    "    node_f = area_df[['total_space','monthly_fee','building_type']]\n",
    "    node_f['total_space'] = node_f.total_space/node_f.total_space.max()\n",
    "    node_f['monthly_fee'] = node_f.monthly_fee/node_f.monthly_fee.max()\n",
    "    building_type_oneHot = pd.get_dummies(node_f['building_type'])\n",
    "    node_f = node_f.drop('building_type',axis = 1)\n",
    "    node_f = node_f.join(building_type_oneHot)\n",
    "    return node_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mark/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/home/mark/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "node_f = get_nodes_features(target_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from abc import ABC\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dropout, GRU, Flatten, Dense, LeakyReLU\n",
    "from spektral.layers import GraphConv\n",
    "\n",
    "l2_reg = 5e-4 / 2  # L2 regularization rate\n",
    "\n",
    "\n",
    "class Generator(Model, ABC):\n",
    "\n",
    "    def __init__(self, adj, nodes_features):\n",
    "        super(Generator, self).__init__()\n",
    "        self.adj = adj\n",
    "        self.nodes_features = nodes_features\n",
    "\n",
    "        self.dropout = Dropout(0.5)\n",
    "        self.flatten = Flatten()\n",
    "        self.graph_conv_1 = GraphConv(32,\n",
    "                                      activation='elu',\n",
    "                                      kernel_regularizer=l2(l2_reg),\n",
    "                                      use_bias=False)\n",
    "        self.graph_conv_2 = GraphConv(16,\n",
    "                                      activation='elu',\n",
    "                                      kernel_regularizer=l2(l2_reg),\n",
    "                                      use_bias=False)\n",
    "        self.dense_1 = Dense(64, activation='relu')\n",
    "        self.dense_2 = Dense(128, activation='relu')\n",
    "        self.gru = GRU(128, return_sequences=True)\n",
    "        self.final_dense = Dense(1, activation='tanh')\n",
    "\n",
    "    def call(self, seq, training=True):\n",
    "        f = tf.convert_to_tensor(self.nodes_features)  # 11*F\n",
    "        g = tf.convert_to_tensor(self.adj)  # 11*11\n",
    "        s = tf.convert_to_tensor(seq)  # 96*11\n",
    "\n",
    "        c = self.graph_conv_1([f, g])  # 11*11\n",
    "        c = self.graph_conv_2([c, g])  # 11*11\n",
    "        s = tf.matmul(s, c)  # 96*11\n",
    "\n",
    "        fc = self.dense_1(s)  # 96*32\n",
    "        fc = self.dropout(fc, training=training)\n",
    "        fc = self.dense_2(fc)  # 96*32\n",
    "        fc = self.dropout(fc, training=training)\n",
    "\n",
    "        fc = tf.expand_dims(fc, axis=0)  # 1*96*32\n",
    "        ro = self.gru(fc)\n",
    "        ro = tf.squeeze(ro, axis=0)  # 96*32\n",
    "        return self.final_dense(ro)  # 96*1\n",
    "\n",
    "\n",
    "class Discriminator(Model, ABC):\n",
    "\n",
    "    def __init__(self, adj, nodes_features):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.adj = adj\n",
    "        self.nodes_features = nodes_features\n",
    "\n",
    "        self.leaky_relu = LeakyReLU(alpha=0.2)\n",
    "        self.dropout = Dropout(0.5)\n",
    "        self.flatten = Flatten()\n",
    "        self.graph_conv_1 = GraphConv(32,\n",
    "                                      activation='elu',\n",
    "                                      kernel_regularizer=l2(l2_reg),\n",
    "                                      use_bias=False)\n",
    "        self.graph_conv_2 = GraphConv(16,\n",
    "                                      activation='elu',\n",
    "                                      kernel_regularizer=l2(l2_reg),\n",
    "                                      use_bias=False)\n",
    "        self.dense_1 = Dense(64)\n",
    "        self.dense_2 = Dense(128)\n",
    "        self.gru = GRU(128, return_sequences=True)\n",
    "        self.final_dense = Dense(1, activation='sigmoid')\n",
    "\n",
    "    def call(self, seq, training=True):\n",
    "        f = tf.convert_to_tensor(self.nodes_features)  # 11*F\n",
    "        g = tf.convert_to_tensor(self.adj)  # 11*11\n",
    "        s = tf.convert_to_tensor(seq)  # 96*11\n",
    "\n",
    "        c = self.graph_conv_1([f, g])  # 11*11\n",
    "        c = self.graph_conv_2([c, g])  # 11*11\n",
    "        s = tf.matmul(s, c)  # 96*11\n",
    "\n",
    "        fc = self.dense_1(s)  # 96*32\n",
    "        fc = self.leaky_relu(fc)\n",
    "        fc = self.dropout(fc, training=training)\n",
    "        fc = self.dense_2(fc)  # 96*64\n",
    "        fc = self.leaky_relu(fc)\n",
    "        fc = self.dropout(fc, training=training)\n",
    "\n",
    "        fc = tf.expand_dims(fc, axis=0)\n",
    "        ro = self.gru(fc)\n",
    "        ro = tf.squeeze(ro, axis=0)  # 96*64\n",
    "        return self.final_dense(ro)  # 96*1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from spektral.utils import normalized_laplacian\n",
    "# from model import Generator, Discriminator\n",
    "import time\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "batch_size = 96\n",
    "save_interval = 5\n",
    "learning_rate = 2e-3\n",
    "adam_beta_1 = 0.6\n",
    "\n",
    "\n",
    "class Train:\n",
    "    def __init__(self, seqs, adj, nodes_features, epochs=1000, key=9):\n",
    "        self.epochs = epochs\n",
    "        self.seqs = seqs.astype('float32')\n",
    "\n",
    "        self.gen_optimizer = Adam(learning_rate, adam_beta_1)\n",
    "        self.desc_optimizer = Adam(learning_rate, adam_beta_1)\n",
    "\n",
    "        self.adj = normalized_laplacian(adj.astype('float32'))\n",
    "        self.nodes_features = nodes_features.astype('float32')\n",
    "        self.generator = Generator(self.adj, self.nodes_features)\n",
    "        self.discriminator = Discriminator(self.adj, self.nodes_features)\n",
    "\n",
    "        self.cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
    "\n",
    "        self.key = key\n",
    "\n",
    "    def __call__(self, epochs=None ,save_path='generated/saved'):\n",
    "        save_path += str(time.time())\n",
    "        if not os.path.exists(save_path):\n",
    "            os.makedirs(save_path)\n",
    "        if epochs is None:\n",
    "            epochs = self.epochs\n",
    "            \n",
    "        time_len = self.seqs.shape[0]\n",
    "        num_nodes = self.seqs.shape[1]\n",
    "        total_batch = int(time_len / batch_size)  # 2976/96=31\n",
    "\n",
    "        time_consumed_total = 0.\n",
    "        for epoch in range(1, epochs + 1):\n",
    "            start = time.time()\n",
    "            total_gen_loss = 0\n",
    "            total_disc_loss = 0\n",
    "\n",
    "            for week in range(0, total_batch):\n",
    "                current_seqs = self.seqs[week*batch_size:week*batch_size + batch_size]\n",
    "                seqs_noised = current_seqs.copy()\n",
    "                max_s = current_seqs[self.key].max()\n",
    "                seqs_noised[self.key] = np.random.normal(max_s / 2.0, max_s / 10.0,\n",
    "                                                         size=(current_seqs.shape[0])).astype('float32')\n",
    "                gen_loss, disc_loss = self.train_step(current_seqs, seqs_noised)\n",
    "                total_gen_loss += gen_loss\n",
    "                total_disc_loss += disc_loss\n",
    "\n",
    "            time_consumed = time.time() - start\n",
    "            time_consumed_total += time_consumed\n",
    "            time_consumed_agv = time_consumed_total / epoch\n",
    "            epochs_last = epochs - epoch\n",
    "            estimate_time_last = epochs_last * time_consumed_agv\n",
    "            print('epoch {}({})/{}({}) - gen_loss = {}, disc_loss = {}, estimated to finish: {}'\n",
    "                  .format(epoch, round(time.time() - start, 2),\n",
    "                          epochs, round(time_consumed_total, 2),\n",
    "                          round(float(total_gen_loss / total_batch), 5),\n",
    "                          round(float(total_disc_loss / total_batch), 5),\n",
    "                          round(estimate_time_last, 2)))\n",
    "\n",
    "            if epoch % save_interval == 0:\n",
    "                plot = self.generate().plot()\n",
    "                fig = plot.get_figure()\n",
    "                fig.savefig(save_path + \"/gen_\" + str(epoch) + \".png\")\n",
    "                plt.clf()\n",
    "\n",
    "    @tf.function\n",
    "    def train_step(self, seqs, seqs_noised):\n",
    "        with tf.GradientTape(persistent=True) as tape:\n",
    "            real_output = self.discriminator(seqs)  # 评价高\n",
    "            generated = self.generator(seqs_noised)\n",
    "            left = tf.slice(seqs, [0, 0], [batch_size, self.key])\n",
    "            right = tf.slice(seqs, [0, self.key + 1], [batch_size, -1])\n",
    "            combined = tf.concat([left, generated, right], 1)\n",
    "            generated_output = self.discriminator(combined)  # 初始评价低\n",
    "\n",
    "            loss_g = self.generator_loss(self.cross_entropy, generated_output)\n",
    "            loss_d = self.discriminator_loss(self.cross_entropy, real_output, generated_output)\n",
    "\n",
    "        grad_gen = tape.gradient(loss_g, self.generator.trainable_variables)\n",
    "        grad_disc = tape.gradient(loss_d, self.discriminator.trainable_variables)\n",
    "\n",
    "        self.gen_optimizer.apply_gradients(zip(grad_gen, self.generator.trainable_variables))\n",
    "        self.desc_optimizer.apply_gradients(zip(grad_disc, self.discriminator.trainable_variables))\n",
    "\n",
    "        return loss_g, loss_d\n",
    "\n",
    "    def generate(self):\n",
    "        import random as random\n",
    "        week = 1 # random.randint(0,31)\n",
    "        seqs_replace = self.seqs[week*batch_size:week*batch_size + batch_size].copy()\n",
    "        max_s = seqs_replace[self.key].max()\n",
    "        seqs_replace[self.key] = np.random.normal(max_s / 2.0, max_s / 10.0, size=(seqs_replace.shape[0])).astype(\n",
    "            'float32')\n",
    "        gen_data = self.generator(seqs_replace, training=False)\n",
    "\n",
    "        from sklearn import preprocessing\n",
    "        min_max_scaler = preprocessing.MinMaxScaler()\n",
    "        x_scaled = min_max_scaler.fit_transform(gen_data.numpy())\n",
    "        return pd.DataFrame(x_scaled) # [self.key]\n",
    "\n",
    "    @staticmethod\n",
    "    def discriminator_loss(loss_object, real_output, fake_output):\n",
    "        \"\"\"\n",
    "        ...\n",
    "        \"\"\"\n",
    "        real_loss = loss_object(tf.ones_like(real_output), real_output)\n",
    "        fake_loss = loss_object(tf.zeros_like(fake_output), fake_output)\n",
    "        total_loss = real_loss + fake_loss\n",
    "        return total_loss\n",
    "\n",
    "    @staticmethod\n",
    "    def generator_loss(loss_object, fake_output):\n",
    "        \"\"\"\n",
    "        ...\n",
    "        \"\"\"\n",
    "        return loss_object(tf.ones_like(fake_output), fake_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1(2.93)/1500(2.93) - gen_loss = 0.58916, disc_loss = 1.38942, estimated to finish: 4391.13\n",
      "epoch 2(0.76)/1500(3.69) - gen_loss = 0.58663, disc_loss = 1.39429, estimated to finish: 2764.06\n",
      "epoch 3(0.8)/1500(4.49) - gen_loss = 0.52124, disc_loss = 1.39462, estimated to finish: 2241.22\n",
      "epoch 4(0.77)/1500(5.26) - gen_loss = 0.57692, disc_loss = 1.38174, estimated to finish: 1966.6\n",
      "epoch 5(0.77)/1500(6.03) - gen_loss = 0.34074, disc_loss = 1.65846, estimated to finish: 1802.69\n",
      "epoch 6(0.78)/1500(6.81) - gen_loss = 0.31338, disc_loss = 1.62676, estimated to finish: 1694.96\n",
      "epoch 7(0.76)/1500(7.56) - gen_loss = 0.31334, disc_loss = 1.6267, estimated to finish: 1613.11\n",
      "epoch 8(0.75)/1500(8.32) - gen_loss = 0.31331, disc_loss = 1.62661, estimated to finish: 1551.18\n",
      "epoch 9(0.76)/1500(9.08) - gen_loss = 0.31333, disc_loss = 1.62662, estimated to finish: 1503.75\n",
      "epoch 10(0.76)/1500(9.84) - gen_loss = 0.31334, disc_loss = 1.62653, estimated to finish: 1465.64\n",
      "epoch 11(0.77)/1500(10.61) - gen_loss = 0.31336, disc_loss = 1.62651, estimated to finish: 1435.71\n",
      "epoch 12(0.78)/1500(11.38) - gen_loss = 0.31335, disc_loss = 1.62646, estimated to finish: 1411.71\n",
      "epoch 13(0.76)/1500(12.15) - gen_loss = 0.31342, disc_loss = 1.62637, estimated to finish: 1389.44\n",
      "epoch 14(0.94)/1500(13.09) - gen_loss = 0.31368, disc_loss = 1.62576, estimated to finish: 1389.19\n",
      "epoch 15(0.8)/1500(13.89) - gen_loss = 0.58482, disc_loss = 1.22348, estimated to finish: 1375.14\n",
      "epoch 16(0.94)/1500(14.84) - gen_loss = 0.65599, disc_loss = 1.33984, estimated to finish: 1375.96\n",
      "epoch 17(0.77)/1500(15.6) - gen_loss = 0.65429, disc_loss = 1.26025, estimated to finish: 1361.05\n",
      "epoch 18(0.78)/1500(16.38) - gen_loss = 0.67985, disc_loss = 1.17667, estimated to finish: 1348.74\n",
      "epoch 19(0.79)/1500(17.18) - gen_loss = 0.61912, disc_loss = 1.23216, estimated to finish: 1338.85\n",
      "epoch 20(0.79)/1500(17.96) - gen_loss = 0.52211, disc_loss = 1.37228, estimated to finish: 1329.39\n",
      "epoch 21(0.77)/1500(18.73) - gen_loss = 0.67512, disc_loss = 1.24811, estimated to finish: 1319.25\n",
      "epoch 22(0.75)/1500(19.49) - gen_loss = 0.67981, disc_loss = 1.1901, estimated to finish: 1309.07\n",
      "epoch 23(0.79)/1500(20.27) - gen_loss = 0.66064, disc_loss = 1.24583, estimated to finish: 1301.81\n",
      "epoch 24(0.77)/1500(21.04) - gen_loss = 0.69282, disc_loss = 1.13329, estimated to finish: 1294.22\n",
      "epoch 25(0.78)/1500(21.82) - gen_loss = 0.64584, disc_loss = 1.08714, estimated to finish: 1287.53\n",
      "epoch 26(0.78)/1500(22.6) - gen_loss = 0.53611, disc_loss = 1.33417, estimated to finish: 1281.45\n",
      "epoch 27(0.76)/1500(23.36) - gen_loss = 0.53923, disc_loss = 1.26138, estimated to finish: 1274.62\n",
      "epoch 28(0.77)/1500(24.13) - gen_loss = 0.57684, disc_loss = 1.33396, estimated to finish: 1268.74\n",
      "epoch 29(0.86)/1500(24.99) - gen_loss = 0.60548, disc_loss = 1.3977, estimated to finish: 1267.81\n",
      "epoch 30(0.87)/1500(25.87) - gen_loss = 0.5987, disc_loss = 1.40083, estimated to finish: 1267.43\n",
      "epoch 31(0.91)/1500(26.78) - gen_loss = 0.65522, disc_loss = 1.30974, estimated to finish: 1268.94\n",
      "epoch 32(0.82)/1500(27.6) - gen_loss = 0.63006, disc_loss = 1.36943, estimated to finish: 1266.11\n",
      "epoch 33(0.86)/1500(28.46) - gen_loss = 0.68503, disc_loss = 1.38788, estimated to finish: 1265.35\n",
      "epoch 34(0.86)/1500(29.32) - gen_loss = 0.68914, disc_loss = 1.38638, estimated to finish: 1264.38\n",
      "epoch 35(0.79)/1500(30.11) - gen_loss = 0.68892, disc_loss = 1.38639, estimated to finish: 1260.29\n",
      "epoch 36(0.83)/1500(30.94) - gen_loss = 0.68962, disc_loss = 1.38623, estimated to finish: 1258.34\n",
      "epoch 37(0.77)/1500(31.72) - gen_loss = 0.6886, disc_loss = 1.38643, estimated to finish: 1254.05\n",
      "epoch 38(0.84)/1500(32.56) - gen_loss = 0.67446, disc_loss = 1.36679, estimated to finish: 1252.64\n",
      "epoch 39(0.78)/1500(33.34) - gen_loss = 0.59585, disc_loss = 1.39991, estimated to finish: 1248.89\n",
      "epoch 40(0.77)/1500(34.11) - gen_loss = 0.68848, disc_loss = 1.38781, estimated to finish: 1245.03\n",
      "epoch 41(0.77)/1500(34.88) - gen_loss = 0.69068, disc_loss = 1.3858, estimated to finish: 1241.18\n",
      "epoch 42(0.75)/1500(35.63) - gen_loss = 0.69044, disc_loss = 1.38645, estimated to finish: 1236.89\n",
      "epoch 43(0.76)/1500(36.39) - gen_loss = 0.69031, disc_loss = 1.38653, estimated to finish: 1232.9\n",
      "epoch 44(0.76)/1500(37.14) - gen_loss = 0.6905, disc_loss = 1.38621, estimated to finish: 1229.06\n",
      "epoch 45(0.81)/1500(37.96) - gen_loss = 0.69021, disc_loss = 1.38626, estimated to finish: 1227.22\n",
      "epoch 46(0.82)/1500(38.78) - gen_loss = 0.69021, disc_loss = 1.38598, estimated to finish: 1225.74\n",
      "epoch 47(0.82)/1500(39.6) - gen_loss = 0.68958, disc_loss = 1.38617, estimated to finish: 1224.18\n",
      "epoch 48(0.85)/1500(40.45) - gen_loss = 0.68902, disc_loss = 1.38598, estimated to finish: 1223.64\n",
      "epoch 49(0.85)/1500(41.3) - gen_loss = 0.68816, disc_loss = 1.38569, estimated to finish: 1222.87\n",
      "epoch 50(0.77)/1500(42.07) - gen_loss = 0.68689, disc_loss = 1.38685, estimated to finish: 1219.97\n",
      "epoch 51(0.82)/1500(42.88) - gen_loss = 0.68847, disc_loss = 1.38679, estimated to finish: 1218.42\n",
      "epoch 52(0.81)/1500(43.69) - gen_loss = 0.68933, disc_loss = 1.38639, estimated to finish: 1216.62\n",
      "epoch 53(0.76)/1500(44.45) - gen_loss = 0.68988, disc_loss = 1.38643, estimated to finish: 1213.47\n",
      "epoch 54(0.75)/1500(45.2) - gen_loss = 0.69015, disc_loss = 1.38633, estimated to finish: 1210.23\n",
      "epoch 55(0.75)/1500(45.95) - gen_loss = 0.69008, disc_loss = 1.38669, estimated to finish: 1207.23\n",
      "epoch 56(0.77)/1500(46.72) - gen_loss = 0.69051, disc_loss = 1.38647, estimated to finish: 1204.65\n",
      "epoch 57(0.76)/1500(47.47) - gen_loss = 0.69069, disc_loss = 1.38645, estimated to finish: 1201.82\n",
      "epoch 58(0.75)/1500(48.22) - gen_loss = 0.69082, disc_loss = 1.38616, estimated to finish: 1198.97\n",
      "epoch 59(0.75)/1500(48.98) - gen_loss = 0.69087, disc_loss = 1.38622, estimated to finish: 1196.26\n",
      "epoch 60(0.76)/1500(49.74) - gen_loss = 0.6903, disc_loss = 1.38648, estimated to finish: 1193.71\n",
      "epoch 61(0.75)/1500(50.49) - gen_loss = 0.69055, disc_loss = 1.38612, estimated to finish: 1191.1\n",
      "epoch 62(0.75)/1500(51.24) - gen_loss = 0.68875, disc_loss = 1.38346, estimated to finish: 1188.52\n",
      "epoch 63(0.75)/1500(51.99) - gen_loss = 0.68622, disc_loss = 1.38438, estimated to finish: 1185.9\n",
      "epoch 64(0.78)/1500(52.77) - gen_loss = 0.66941, disc_loss = 1.38557, estimated to finish: 1184.04\n",
      "epoch 65(0.85)/1500(53.62) - gen_loss = 0.683, disc_loss = 1.37584, estimated to finish: 1183.67\n",
      "epoch 66(0.86)/1500(54.48) - gen_loss = 0.65947, disc_loss = 1.3873, estimated to finish: 1183.68\n",
      "epoch 67(0.83)/1500(55.31) - gen_loss = 0.69116, disc_loss = 1.35706, estimated to finish: 1182.93\n",
      "epoch 68(0.98)/1500(56.29) - gen_loss = 0.5509, disc_loss = 1.4023, estimated to finish: 1185.35\n",
      "epoch 69(0.84)/1500(57.12) - gen_loss = 0.66817, disc_loss = 1.37089, estimated to finish: 1184.68\n",
      "epoch 70(1.0)/1500(58.12) - gen_loss = 0.5993, disc_loss = 1.3878, estimated to finish: 1187.3\n",
      "epoch 71(1.02)/1500(59.14) - gen_loss = 0.6696, disc_loss = 1.25095, estimated to finish: 1190.36\n",
      "epoch 72(0.85)/1500(59.99) - gen_loss = 0.69284, disc_loss = 1.29244, estimated to finish: 1189.85\n",
      "epoch 73(0.76)/1500(60.75) - gen_loss = 0.51547, disc_loss = 1.33566, estimated to finish: 1187.59\n",
      "epoch 74(0.79)/1500(61.54) - gen_loss = 0.64234, disc_loss = 1.35889, estimated to finish: 1185.92\n",
      "epoch 75(0.83)/1500(62.37) - gen_loss = 0.68193, disc_loss = 1.38779, estimated to finish: 1185.09\n",
      "epoch 76(0.85)/1500(63.22) - gen_loss = 0.671, disc_loss = 1.35733, estimated to finish: 1184.59\n",
      "epoch 77(0.92)/1500(64.14) - gen_loss = 0.66592, disc_loss = 1.38116, estimated to finish: 1185.39\n",
      "epoch 78(0.96)/1500(65.1) - gen_loss = 0.6828, disc_loss = 1.38646, estimated to finish: 1186.87\n",
      "epoch 79(1.02)/1500(66.12) - gen_loss = 0.67731, disc_loss = 1.38186, estimated to finish: 1189.37\n",
      "epoch 80(0.97)/1500(67.09) - gen_loss = 0.67002, disc_loss = 1.38482, estimated to finish: 1190.89\n",
      "epoch 81(0.84)/1500(67.94) - gen_loss = 0.66418, disc_loss = 1.38455, estimated to finish: 1190.15\n",
      "epoch 82(0.87)/1500(68.81) - gen_loss = 0.66199, disc_loss = 1.38452, estimated to finish: 1189.9\n",
      "epoch 83(0.93)/1500(69.74) - gen_loss = 0.66645, disc_loss = 1.3883, estimated to finish: 1190.69\n",
      "epoch 84(0.79)/1500(70.53) - gen_loss = 0.67533, disc_loss = 1.38548, estimated to finish: 1188.94\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 85(0.76)/1500(71.29) - gen_loss = 0.63944, disc_loss = 1.37767, estimated to finish: 1186.75\n",
      "epoch 86(0.76)/1500(72.05) - gen_loss = 0.64537, disc_loss = 1.37859, estimated to finish: 1184.69\n",
      "epoch 87(0.76)/1500(72.81) - gen_loss = 0.68184, disc_loss = 1.38826, estimated to finish: 1182.56\n",
      "epoch 88(0.76)/1500(73.57) - gen_loss = 0.67941, disc_loss = 1.37847, estimated to finish: 1180.45\n",
      "epoch 89(0.75)/1500(74.32) - gen_loss = 0.63366, disc_loss = 1.39421, estimated to finish: 1178.31\n",
      "epoch 90(0.77)/1500(75.09) - gen_loss = 0.67463, disc_loss = 1.38327, estimated to finish: 1176.41\n",
      "epoch 91(0.78)/1500(75.87) - gen_loss = 0.6547, disc_loss = 1.38454, estimated to finish: 1174.66\n",
      "epoch 92(0.76)/1500(76.62) - gen_loss = 0.66099, disc_loss = 1.38206, estimated to finish: 1172.68\n",
      "epoch 93(0.76)/1500(77.38) - gen_loss = 0.62824, disc_loss = 1.37557, estimated to finish: 1170.67\n",
      "epoch 94(0.75)/1500(78.13) - gen_loss = 0.64749, disc_loss = 1.38136, estimated to finish: 1168.63\n",
      "epoch 95(0.77)/1500(78.9) - gen_loss = 0.68502, disc_loss = 1.38807, estimated to finish: 1166.92\n",
      "epoch 96(0.89)/1500(79.8) - gen_loss = 0.68935, disc_loss = 1.38599, estimated to finish: 1167.02\n",
      "epoch 97(0.92)/1500(80.71) - gen_loss = 0.68667, disc_loss = 1.38388, estimated to finish: 1167.42\n",
      "epoch 98(0.82)/1500(81.53) - gen_loss = 0.65696, disc_loss = 1.3587, estimated to finish: 1166.37\n",
      "epoch 99(0.82)/1500(82.35) - gen_loss = 0.62399, disc_loss = 1.35555, estimated to finish: 1165.35\n",
      "epoch 100(0.78)/1500(83.13) - gen_loss = 0.66504, disc_loss = 1.39371, estimated to finish: 1163.78\n",
      "epoch 101(0.79)/1500(83.92) - gen_loss = 0.68113, disc_loss = 1.38729, estimated to finish: 1162.39\n",
      "epoch 102(0.77)/1500(84.69) - gen_loss = 0.68269, disc_loss = 1.38755, estimated to finish: 1160.78\n",
      "epoch 103(0.83)/1500(85.52) - gen_loss = 0.68647, disc_loss = 1.38656, estimated to finish: 1159.97\n",
      "epoch 104(0.83)/1500(86.35) - gen_loss = 0.68661, disc_loss = 1.38588, estimated to finish: 1159.08\n",
      "epoch 105(0.78)/1500(87.13) - gen_loss = 0.68228, disc_loss = 1.38251, estimated to finish: 1157.59\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mark/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py:522: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 106(0.87)/1500(88.0) - gen_loss = 0.62338, disc_loss = 1.36979, estimated to finish: 1157.31\n",
      "epoch 107(0.78)/1500(88.78) - gen_loss = 0.68487, disc_loss = 1.38642, estimated to finish: 1155.79\n",
      "epoch 108(0.84)/1500(89.62) - gen_loss = 0.68695, disc_loss = 1.38526, estimated to finish: 1155.13\n",
      "epoch 109(0.82)/1500(90.44) - gen_loss = 0.68394, disc_loss = 1.38595, estimated to finish: 1154.12\n",
      "epoch 110(0.84)/1500(91.28) - gen_loss = 0.68268, disc_loss = 1.38579, estimated to finish: 1153.44\n",
      "epoch 111(0.76)/1500(92.04) - gen_loss = 0.67148, disc_loss = 1.38494, estimated to finish: 1151.75\n",
      "epoch 112(0.76)/1500(92.8) - gen_loss = 0.67248, disc_loss = 1.38337, estimated to finish: 1150.0\n",
      "epoch 113(0.75)/1500(93.54) - gen_loss = 0.65417, disc_loss = 1.38329, estimated to finish: 1148.18\n",
      "epoch 114(0.75)/1500(94.3) - gen_loss = 0.65497, disc_loss = 1.38408, estimated to finish: 1146.46\n",
      "epoch 115(0.77)/1500(95.07) - gen_loss = 0.6528, disc_loss = 1.37904, estimated to finish: 1144.94\n",
      "epoch 116(0.77)/1500(95.84) - gen_loss = 0.6222, disc_loss = 1.38454, estimated to finish: 1143.49\n",
      "epoch 117(0.77)/1500(96.61) - gen_loss = 0.64552, disc_loss = 1.29463, estimated to finish: 1141.95\n",
      "epoch 118(0.75)/1500(97.36) - gen_loss = 0.66957, disc_loss = 1.39616, estimated to finish: 1140.28\n",
      "epoch 119(0.75)/1500(98.12) - gen_loss = 0.63673, disc_loss = 1.32197, estimated to finish: 1138.63\n",
      "epoch 120(0.77)/1500(98.89) - gen_loss = 0.6486, disc_loss = 1.2996, estimated to finish: 1137.23\n",
      "epoch 121(0.79)/1500(99.68) - gen_loss = 0.64007, disc_loss = 1.30538, estimated to finish: 1136.03\n",
      "epoch 122(0.76)/1500(100.44) - gen_loss = 0.67775, disc_loss = 1.39292, estimated to finish: 1134.45\n",
      "epoch 123(0.81)/1500(101.24) - gen_loss = 0.69129, disc_loss = 1.38679, estimated to finish: 1133.45\n",
      "epoch 124(0.88)/1500(102.12) - gen_loss = 0.69198, disc_loss = 1.38654, estimated to finish: 1133.22\n",
      "epoch 125(0.79)/1500(102.91) - gen_loss = 0.69229, disc_loss = 1.38634, estimated to finish: 1132.05\n",
      "epoch 126(0.92)/1500(103.84) - gen_loss = 0.69234, disc_loss = 1.38634, estimated to finish: 1132.33\n",
      "epoch 127(1.1)/1500(104.93) - gen_loss = 0.69235, disc_loss = 1.38636, estimated to finish: 1134.45\n",
      "epoch 128(0.96)/1500(105.89) - gen_loss = 0.69251, disc_loss = 1.38621, estimated to finish: 1135.03\n",
      "epoch 129(0.81)/1500(106.7) - gen_loss = 0.69248, disc_loss = 1.38629, estimated to finish: 1134.02\n",
      "epoch 130(0.85)/1500(107.55) - gen_loss = 0.69248, disc_loss = 1.38635, estimated to finish: 1133.42\n",
      "epoch 131(0.78)/1500(108.33) - gen_loss = 0.69275, disc_loss = 1.38619, estimated to finish: 1132.11\n",
      "epoch 132(0.75)/1500(109.09) - gen_loss = 0.69278, disc_loss = 1.38611, estimated to finish: 1130.52\n",
      "epoch 133(0.77)/1500(109.86) - gen_loss = 0.69281, disc_loss = 1.38605, estimated to finish: 1129.12\n",
      "epoch 134(0.78)/1500(110.64) - gen_loss = 0.69274, disc_loss = 1.38599, estimated to finish: 1127.84\n",
      "epoch 135(0.75)/1500(111.39) - gen_loss = 0.69258, disc_loss = 1.38605, estimated to finish: 1126.27\n",
      "epoch 136(0.8)/1500(112.19) - gen_loss = 0.69253, disc_loss = 1.38586, estimated to finish: 1125.19\n",
      "epoch 137(0.77)/1500(112.96) - gen_loss = 0.69231, disc_loss = 1.38569, estimated to finish: 1123.86\n",
      "epoch 138(0.82)/1500(113.79) - gen_loss = 0.69174, disc_loss = 1.38573, estimated to finish: 1123.01\n",
      "epoch 139(0.77)/1500(114.56) - gen_loss = 0.69071, disc_loss = 1.38589, estimated to finish: 1121.66\n",
      "epoch 140(0.85)/1500(115.4) - gen_loss = 0.69043, disc_loss = 1.3859, estimated to finish: 1121.05\n",
      "epoch 141(0.77)/1500(116.17) - gen_loss = 0.68773, disc_loss = 1.38447, estimated to finish: 1119.67\n",
      "epoch 142(0.77)/1500(116.94) - gen_loss = 0.64967, disc_loss = 1.36306, estimated to finish: 1118.36\n",
      "epoch 143(0.78)/1500(117.72) - gen_loss = 0.6384, disc_loss = 1.31208, estimated to finish: 1117.09\n",
      "epoch 144(0.77)/1500(118.49) - gen_loss = 0.64919, disc_loss = 1.28598, estimated to finish: 1115.81\n",
      "epoch 145(0.75)/1500(119.25) - gen_loss = 0.6591, disc_loss = 1.27252, estimated to finish: 1114.35\n",
      "epoch 146(0.79)/1500(120.03) - gen_loss = 0.69175, disc_loss = 1.30033, estimated to finish: 1113.18\n",
      "epoch 147(0.77)/1500(120.8) - gen_loss = 0.50232, disc_loss = 1.36079, estimated to finish: 1111.84\n",
      "epoch 148(0.82)/1500(121.62) - gen_loss = 0.65932, disc_loss = 1.34253, estimated to finish: 1110.99\n",
      "epoch 149(0.77)/1500(122.39) - gen_loss = 0.67168, disc_loss = 1.3831, estimated to finish: 1109.7\n",
      "epoch 150(0.8)/1500(123.19) - gen_loss = 0.6415, disc_loss = 1.37731, estimated to finish: 1108.68\n",
      "epoch 151(0.8)/1500(123.98) - gen_loss = 0.66279, disc_loss = 1.38555, estimated to finish: 1107.65\n",
      "epoch 152(0.79)/1500(124.78) - gen_loss = 0.65669, disc_loss = 1.37333, estimated to finish: 1106.57\n",
      "epoch 153(0.79)/1500(125.57) - gen_loss = 0.67444, disc_loss = 1.37354, estimated to finish: 1105.5\n",
      "epoch 154(0.82)/1500(126.39) - gen_loss = 0.6246, disc_loss = 1.29612, estimated to finish: 1104.71\n",
      "epoch 155(0.78)/1500(127.17) - gen_loss = 0.64879, disc_loss = 1.33387, estimated to finish: 1103.52\n",
      "epoch 156(0.76)/1500(127.94) - gen_loss = 0.68164, disc_loss = 1.36237, estimated to finish: 1102.21\n",
      "epoch 157(0.81)/1500(128.74) - gen_loss = 0.64538, disc_loss = 1.3219, estimated to finish: 1101.27\n",
      "epoch 158(0.8)/1500(129.54) - gen_loss = 0.54333, disc_loss = 1.38256, estimated to finish: 1100.25\n",
      "epoch 159(0.83)/1500(130.37) - gen_loss = 0.61635, disc_loss = 1.36889, estimated to finish: 1099.51\n",
      "epoch 160(0.82)/1500(131.18) - gen_loss = 0.66645, disc_loss = 1.28978, estimated to finish: 1098.66\n",
      "epoch 161(0.76)/1500(131.94) - gen_loss = 0.56766, disc_loss = 1.41881, estimated to finish: 1097.33\n",
      "epoch 162(0.76)/1500(132.7) - gen_loss = 0.67442, disc_loss = 1.23852, estimated to finish: 1095.99\n",
      "epoch 163(0.75)/1500(133.45) - gen_loss = 0.67677, disc_loss = 1.23327, estimated to finish: 1094.63\n",
      "epoch 164(0.79)/1500(134.24) - gen_loss = 0.61735, disc_loss = 1.34338, estimated to finish: 1093.55\n",
      "epoch 165(0.76)/1500(135.0) - gen_loss = 0.60135, disc_loss = 1.39157, estimated to finish: 1092.25\n",
      "epoch 166(0.83)/1500(135.82) - gen_loss = 0.64635, disc_loss = 1.36569, estimated to finish: 1091.49\n",
      "epoch 167(0.96)/1500(136.78) - gen_loss = 0.63535, disc_loss = 1.41154, estimated to finish: 1091.8\n",
      "epoch 168(0.8)/1500(137.59) - gen_loss = 0.66866, disc_loss = 1.34801, estimated to finish: 1090.87\n",
      "epoch 169(0.83)/1500(138.42) - gen_loss = 0.65656, disc_loss = 1.34865, estimated to finish: 1090.14\n",
      "epoch 170(0.91)/1500(139.33) - gen_loss = 0.65778, disc_loss = 1.36079, estimated to finish: 1090.03\n",
      "epoch 171(0.85)/1500(140.18) - gen_loss = 0.55516, disc_loss = 1.42014, estimated to finish: 1089.47\n",
      "epoch 172(0.78)/1500(140.96) - gen_loss = 0.61277, disc_loss = 1.37517, estimated to finish: 1088.32\n",
      "epoch 173(0.83)/1500(141.79) - gen_loss = 0.62544, disc_loss = 1.36475, estimated to finish: 1087.61\n",
      "epoch 174(0.81)/1500(142.6) - gen_loss = 0.64807, disc_loss = 1.37319, estimated to finish: 1086.68\n",
      "epoch 175(0.78)/1500(143.38) - gen_loss = 0.65296, disc_loss = 1.36207, estimated to finish: 1085.57\n",
      "epoch 176(0.83)/1500(144.21) - gen_loss = 0.62947, disc_loss = 1.37942, estimated to finish: 1084.86\n",
      "epoch 177(0.84)/1500(145.05) - gen_loss = 0.64075, disc_loss = 1.37711, estimated to finish: 1084.17\n",
      "epoch 178(0.78)/1500(145.83) - gen_loss = 0.64854, disc_loss = 1.36817, estimated to finish: 1083.09\n",
      "epoch 179(0.79)/1500(146.62) - gen_loss = 0.63979, disc_loss = 1.35537, estimated to finish: 1082.05\n",
      "epoch 180(0.77)/1500(147.39) - gen_loss = 0.59817, disc_loss = 1.34483, estimated to finish: 1080.86\n",
      "epoch 181(0.76)/1500(148.15) - gen_loss = 0.63627, disc_loss = 1.35812, estimated to finish: 1079.63\n",
      "epoch 182(0.77)/1500(148.92) - gen_loss = 0.65449, disc_loss = 1.27043, estimated to finish: 1078.44\n",
      "epoch 183(0.76)/1500(149.67) - gen_loss = 0.65899, disc_loss = 1.25933, estimated to finish: 1077.17\n",
      "epoch 184(0.78)/1500(150.45) - gen_loss = 0.6414, disc_loss = 1.32528, estimated to finish: 1076.06\n",
      "epoch 185(0.75)/1500(151.2) - gen_loss = 0.63748, disc_loss = 1.38275, estimated to finish: 1074.76\n",
      "epoch 186(0.77)/1500(151.97) - gen_loss = 0.658, disc_loss = 1.37359, estimated to finish: 1073.6\n",
      "epoch 187(0.76)/1500(152.73) - gen_loss = 0.65858, disc_loss = 1.36225, estimated to finish: 1072.37\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 188(0.83)/1500(153.56) - gen_loss = 0.64201, disc_loss = 1.34198, estimated to finish: 1071.64\n",
      "epoch 189(0.77)/1500(154.33) - gen_loss = 0.64502, disc_loss = 1.38158, estimated to finish: 1070.5\n",
      "epoch 190(0.77)/1500(155.09) - gen_loss = 0.66365, disc_loss = 1.35182, estimated to finish: 1069.33\n",
      "epoch 191(0.91)/1500(156.0) - gen_loss = 0.65196, disc_loss = 1.36635, estimated to finish: 1069.15\n",
      "epoch 192(0.79)/1500(156.79) - gen_loss = 0.65212, disc_loss = 1.36792, estimated to finish: 1068.13\n",
      "epoch 193(0.8)/1500(157.59) - gen_loss = 0.66136, disc_loss = 1.36518, estimated to finish: 1067.19\n",
      "epoch 194(0.8)/1500(158.39) - gen_loss = 0.65638, disc_loss = 1.37393, estimated to finish: 1066.3\n",
      "epoch 195(0.8)/1500(159.19) - gen_loss = 0.67005, disc_loss = 1.36679, estimated to finish: 1065.34\n",
      "epoch 196(0.82)/1500(160.01) - gen_loss = 0.65362, disc_loss = 1.36336, estimated to finish: 1064.54\n",
      "epoch 197(0.8)/1500(160.8) - gen_loss = 0.67315, disc_loss = 1.3778, estimated to finish: 1063.59\n",
      "epoch 198(0.78)/1500(161.59) - gen_loss = 0.68685, disc_loss = 1.35845, estimated to finish: 1062.55\n",
      "epoch 199(0.79)/1500(162.37) - gen_loss = 0.66192, disc_loss = 1.37184, estimated to finish: 1061.55\n",
      "epoch 200(0.8)/1500(163.18) - gen_loss = 0.66833, disc_loss = 1.36197, estimated to finish: 1060.65\n",
      "epoch 201(0.79)/1500(163.96) - gen_loss = 0.66159, disc_loss = 1.3621, estimated to finish: 1059.64\n",
      "epoch 202(0.76)/1500(164.72) - gen_loss = 0.68122, disc_loss = 1.35363, estimated to finish: 1058.46\n",
      "epoch 203(0.81)/1500(165.53) - gen_loss = 0.64932, disc_loss = 1.36517, estimated to finish: 1057.61\n",
      "epoch 204(0.8)/1500(166.33) - gen_loss = 0.65093, disc_loss = 1.35188, estimated to finish: 1056.71\n",
      "epoch 205(0.76)/1500(167.1) - gen_loss = 0.61836, disc_loss = 1.36514, estimated to finish: 1055.55\n",
      "epoch 206(0.78)/1500(167.87) - gen_loss = 0.61717, disc_loss = 1.34209, estimated to finish: 1054.51\n",
      "epoch 207(0.78)/1500(168.65) - gen_loss = 0.62239, disc_loss = 1.35341, estimated to finish: 1053.46\n",
      "epoch 208(0.82)/1500(169.47) - gen_loss = 0.63579, disc_loss = 1.32112, estimated to finish: 1052.65\n",
      "epoch 209(0.81)/1500(170.28) - gen_loss = 0.63638, disc_loss = 1.31061, estimated to finish: 1051.81\n",
      "epoch 210(0.75)/1500(171.03) - gen_loss = 0.64064, disc_loss = 1.32285, estimated to finish: 1050.62\n",
      "epoch 211(0.82)/1500(171.85) - gen_loss = 0.63626, disc_loss = 1.36921, estimated to finish: 1049.83\n",
      "epoch 212(0.95)/1500(172.8) - gen_loss = 0.65889, disc_loss = 1.3378, estimated to finish: 1049.81\n",
      "epoch 213(0.8)/1500(173.59) - gen_loss = 0.65757, disc_loss = 1.35185, estimated to finish: 1048.89\n",
      "epoch 214(0.79)/1500(174.38) - gen_loss = 0.649, disc_loss = 1.36627, estimated to finish: 1047.89\n",
      "epoch 215(0.75)/1500(175.13) - gen_loss = 0.65915, disc_loss = 1.35388, estimated to finish: 1046.71\n",
      "epoch 216(0.77)/1500(175.9) - gen_loss = 0.67157, disc_loss = 1.35932, estimated to finish: 1045.65\n",
      "epoch 217(0.79)/1500(176.7) - gen_loss = 0.66088, disc_loss = 1.32689, estimated to finish: 1044.7\n",
      "epoch 218(0.8)/1500(177.49) - gen_loss = 0.66578, disc_loss = 1.30954, estimated to finish: 1043.8\n",
      "epoch 219(0.76)/1500(178.26) - gen_loss = 0.64257, disc_loss = 1.35528, estimated to finish: 1042.68\n",
      "epoch 220(0.78)/1500(179.03) - gen_loss = 0.68926, disc_loss = 1.24183, estimated to finish: 1041.66\n",
      "epoch 221(0.77)/1500(179.81) - gen_loss = 0.61955, disc_loss = 1.20052, estimated to finish: 1040.6\n",
      "epoch 222(0.82)/1500(180.63) - gen_loss = 0.63449, disc_loss = 1.3364, estimated to finish: 1039.84\n",
      "epoch 223(0.8)/1500(181.43) - gen_loss = 0.60099, disc_loss = 1.41885, estimated to finish: 1038.94\n",
      "epoch 224(0.8)/1500(182.23) - gen_loss = 0.67536, disc_loss = 1.31167, estimated to finish: 1038.06\n",
      "epoch 225(0.84)/1500(183.07) - gen_loss = 0.59453, disc_loss = 1.35637, estimated to finish: 1037.38\n",
      "epoch 226(0.88)/1500(183.94) - gen_loss = 0.54299, disc_loss = 1.38265, estimated to finish: 1036.92\n",
      "epoch 227(0.78)/1500(184.73) - gen_loss = 0.60433, disc_loss = 1.35313, estimated to finish: 1035.94\n",
      "epoch 228(0.78)/1500(185.5) - gen_loss = 0.65518, disc_loss = 1.32542, estimated to finish: 1034.91\n",
      "epoch 229(0.77)/1500(186.27) - gen_loss = 0.65031, disc_loss = 1.34289, estimated to finish: 1033.83\n",
      "epoch 230(0.86)/1500(187.13) - gen_loss = 0.68336, disc_loss = 1.21297, estimated to finish: 1033.28\n",
      "epoch 231(1.04)/1500(188.17) - gen_loss = 0.50445, disc_loss = 1.41532, estimated to finish: 1033.7\n",
      "epoch 232(0.89)/1500(189.06) - gen_loss = 0.66194, disc_loss = 1.30029, estimated to finish: 1033.3\n",
      "epoch 233(0.85)/1500(189.91) - gen_loss = 0.64041, disc_loss = 1.32797, estimated to finish: 1032.67\n",
      "epoch 234(0.75)/1500(190.66) - gen_loss = 0.55246, disc_loss = 1.32495, estimated to finish: 1031.5\n",
      "epoch 235(0.84)/1500(191.5) - gen_loss = 0.63689, disc_loss = 1.31253, estimated to finish: 1030.83\n",
      "epoch 236(0.76)/1500(192.26) - gen_loss = 0.64398, disc_loss = 1.31134, estimated to finish: 1029.72\n",
      "epoch 237(0.87)/1500(193.13) - gen_loss = 0.64699, disc_loss = 1.31949, estimated to finish: 1029.19\n",
      "epoch 238(0.79)/1500(193.92) - gen_loss = 0.63569, disc_loss = 1.33149, estimated to finish: 1028.24\n",
      "epoch 239(0.8)/1500(194.71) - gen_loss = 0.64869, disc_loss = 1.30214, estimated to finish: 1027.33\n",
      "epoch 240(0.78)/1500(195.49) - gen_loss = 0.64393, disc_loss = 1.35556, estimated to finish: 1026.31\n",
      "epoch 241(0.76)/1500(196.24) - gen_loss = 0.66972, disc_loss = 1.31776, estimated to finish: 1025.19\n",
      "epoch 242(0.77)/1500(197.01) - gen_loss = 0.66282, disc_loss = 1.27872, estimated to finish: 1024.13\n",
      "epoch 243(0.78)/1500(197.79) - gen_loss = 0.62636, disc_loss = 1.30888, estimated to finish: 1023.16\n",
      "epoch 244(0.78)/1500(198.57) - gen_loss = 0.64069, disc_loss = 1.3166, estimated to finish: 1022.14\n",
      "epoch 245(0.79)/1500(199.36) - gen_loss = 0.64384, disc_loss = 1.2849, estimated to finish: 1021.23\n",
      "epoch 246(0.99)/1500(200.35) - gen_loss = 0.65576, disc_loss = 1.24366, estimated to finish: 1021.32\n",
      "epoch 247(0.89)/1500(201.24) - gen_loss = 0.6425, disc_loss = 1.29491, estimated to finish: 1020.89\n",
      "epoch 248(0.88)/1500(202.12) - gen_loss = 0.63977, disc_loss = 1.31975, estimated to finish: 1020.4\n",
      "epoch 249(0.9)/1500(203.03) - gen_loss = 0.66616, disc_loss = 1.32074, estimated to finish: 1020.02\n",
      "epoch 250(0.8)/1500(203.82) - gen_loss = 0.64535, disc_loss = 1.30535, estimated to finish: 1019.1\n",
      "epoch 251(0.76)/1500(204.59) - gen_loss = 0.58247, disc_loss = 1.36059, estimated to finish: 1018.04\n",
      "epoch 252(0.75)/1500(205.34) - gen_loss = 0.61771, disc_loss = 1.35375, estimated to finish: 1016.9\n",
      "epoch 253(0.75)/1500(206.09) - gen_loss = 0.64419, disc_loss = 1.29476, estimated to finish: 1015.79\n",
      "epoch 254(0.76)/1500(206.85) - gen_loss = 0.65968, disc_loss = 1.28943, estimated to finish: 1014.7\n",
      "epoch 255(0.76)/1500(207.61) - gen_loss = 0.64862, disc_loss = 1.26195, estimated to finish: 1013.62\n",
      "epoch 256(0.76)/1500(208.37) - gen_loss = 0.64021, disc_loss = 1.30189, estimated to finish: 1012.56\n",
      "epoch 257(0.78)/1500(209.15) - gen_loss = 0.65454, disc_loss = 1.32753, estimated to finish: 1011.57\n",
      "epoch 258(0.77)/1500(209.92) - gen_loss = 0.63542, disc_loss = 1.31956, estimated to finish: 1010.54\n",
      "epoch 259(0.8)/1500(210.72) - gen_loss = 0.64948, disc_loss = 1.3142, estimated to finish: 1009.65\n",
      "epoch 260(0.79)/1500(211.5) - gen_loss = 0.67721, disc_loss = 1.28855, estimated to finish: 1008.71\n",
      "epoch 261(0.78)/1500(212.28) - gen_loss = 0.64594, disc_loss = 1.15779, estimated to finish: 1007.72\n",
      "epoch 262(0.77)/1500(213.05) - gen_loss = 0.54757, disc_loss = 1.3568, estimated to finish: 1006.69\n",
      "epoch 263(0.8)/1500(213.84) - gen_loss = 0.59877, disc_loss = 1.29249, estimated to finish: 1005.8\n",
      "epoch 264(0.82)/1500(214.66) - gen_loss = 0.65452, disc_loss = 1.25209, estimated to finish: 1005.01\n",
      "epoch 265(0.78)/1500(215.44) - gen_loss = 0.65987, disc_loss = 1.29128, estimated to finish: 1004.04\n",
      "epoch 266(0.81)/1500(216.25) - gen_loss = 0.62948, disc_loss = 1.28339, estimated to finish: 1003.22\n",
      "epoch 267(0.8)/1500(217.05) - gen_loss = 0.63252, disc_loss = 1.28586, estimated to finish: 1002.33\n",
      "epoch 268(0.76)/1500(217.81) - gen_loss = 0.63246, disc_loss = 1.28203, estimated to finish: 1001.3\n",
      "epoch 269(0.79)/1500(218.6) - gen_loss = 0.64308, disc_loss = 1.25102, estimated to finish: 1000.38\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 270(0.8)/1500(219.4) - gen_loss = 0.59965, disc_loss = 1.34248, estimated to finish: 999.49\n",
      "epoch 271(0.79)/1500(220.19) - gen_loss = 0.655, disc_loss = 1.31814, estimated to finish: 998.57\n",
      "epoch 272(0.78)/1500(220.96) - gen_loss = 0.64699, disc_loss = 1.2504, estimated to finish: 997.59\n",
      "epoch 273(0.87)/1500(221.84) - gen_loss = 0.66454, disc_loss = 1.21483, estimated to finish: 997.05\n",
      "epoch 274(0.82)/1500(222.65) - gen_loss = 0.63878, disc_loss = 1.2702, estimated to finish: 996.25\n",
      "epoch 275(0.84)/1500(223.49) - gen_loss = 0.62294, disc_loss = 1.30669, estimated to finish: 995.57\n",
      "epoch 276(0.8)/1500(224.3) - gen_loss = 0.63788, disc_loss = 1.28027, estimated to finish: 994.7\n",
      "epoch 277(0.83)/1500(225.13) - gen_loss = 0.61381, disc_loss = 1.30765, estimated to finish: 993.98\n",
      "epoch 278(0.79)/1500(225.92) - gen_loss = 0.6374, disc_loss = 1.32676, estimated to finish: 993.06\n",
      "epoch 279(0.8)/1500(226.72) - gen_loss = 0.61497, disc_loss = 1.33608, estimated to finish: 992.19\n",
      "epoch 280(0.82)/1500(227.54) - gen_loss = 0.64768, disc_loss = 1.33507, estimated to finish: 991.41\n",
      "epoch 281(0.89)/1500(228.43) - gen_loss = 0.68752, disc_loss = 1.31285, estimated to finish: 990.93\n",
      "epoch 282(0.78)/1500(229.21) - gen_loss = 0.57997, disc_loss = 1.33388, estimated to finish: 989.99\n",
      "epoch 283(0.78)/1500(229.99) - gen_loss = 0.62209, disc_loss = 1.32576, estimated to finish: 989.05\n",
      "epoch 284(0.8)/1500(230.8) - gen_loss = 0.66034, disc_loss = 1.30086, estimated to finish: 988.19\n",
      "epoch 285(0.8)/1500(231.59) - gen_loss = 0.64178, disc_loss = 1.3347, estimated to finish: 987.31\n",
      "epoch 286(0.81)/1500(232.4) - gen_loss = 0.63544, disc_loss = 1.33898, estimated to finish: 986.5\n",
      "epoch 287(0.77)/1500(233.17) - gen_loss = 0.6248, disc_loss = 1.26473, estimated to finish: 985.51\n",
      "epoch 288(0.83)/1500(234.0) - gen_loss = 0.59266, disc_loss = 1.3278, estimated to finish: 984.76\n",
      "epoch 289(0.76)/1500(234.77) - gen_loss = 0.63057, disc_loss = 1.28095, estimated to finish: 983.75\n",
      "epoch 290(0.78)/1500(235.55) - gen_loss = 0.63976, disc_loss = 1.25564, estimated to finish: 982.79\n",
      "epoch 291(0.79)/1500(236.34) - gen_loss = 0.64576, disc_loss = 1.29113, estimated to finish: 981.89\n",
      "epoch 292(0.77)/1500(237.1) - gen_loss = 0.62937, disc_loss = 1.29992, estimated to finish: 980.9\n",
      "epoch 293(0.77)/1500(237.88) - gen_loss = 0.63329, disc_loss = 1.31714, estimated to finish: 979.93\n",
      "epoch 294(0.77)/1500(238.65) - gen_loss = 0.64662, disc_loss = 1.31289, estimated to finish: 978.96\n",
      "epoch 295(0.78)/1500(239.43) - gen_loss = 0.59343, disc_loss = 1.30577, estimated to finish: 978.02\n",
      "epoch 296(0.78)/1500(240.21) - gen_loss = 0.64392, disc_loss = 1.28314, estimated to finish: 977.09\n",
      "epoch 297(0.79)/1500(241.01) - gen_loss = 0.65191, disc_loss = 1.25297, estimated to finish: 976.21\n",
      "epoch 298(0.75)/1500(241.76) - gen_loss = 0.64453, disc_loss = 1.26875, estimated to finish: 975.15\n",
      "epoch 299(0.77)/1500(242.53) - gen_loss = 0.65376, disc_loss = 1.26333, estimated to finish: 974.18\n",
      "epoch 300(0.85)/1500(243.38) - gen_loss = 0.63098, disc_loss = 1.23928, estimated to finish: 973.52\n",
      "epoch 301(0.82)/1500(244.2) - gen_loss = 0.60502, disc_loss = 1.28118, estimated to finish: 972.73\n",
      "epoch 302(0.77)/1500(244.96) - gen_loss = 0.63777, disc_loss = 1.27531, estimated to finish: 971.74\n",
      "epoch 303(0.77)/1500(245.73) - gen_loss = 0.62708, disc_loss = 1.33762, estimated to finish: 970.76\n",
      "epoch 304(0.77)/1500(246.5) - gen_loss = 0.63924, disc_loss = 1.36132, estimated to finish: 969.78\n",
      "epoch 305(0.76)/1500(247.26) - gen_loss = 0.64355, disc_loss = 1.35143, estimated to finish: 968.76\n",
      "epoch 306(0.77)/1500(248.03) - gen_loss = 0.62893, disc_loss = 1.28435, estimated to finish: 967.79\n",
      "epoch 307(0.76)/1500(248.78) - gen_loss = 0.5877, disc_loss = 1.28022, estimated to finish: 966.76\n",
      "epoch 308(0.75)/1500(249.53) - gen_loss = 0.60643, disc_loss = 1.31331, estimated to finish: 965.73\n",
      "epoch 309(0.75)/1500(250.29) - gen_loss = 0.63066, disc_loss = 1.31465, estimated to finish: 964.7\n",
      "epoch 310(0.75)/1500(251.03) - gen_loss = 0.6488, disc_loss = 1.3334, estimated to finish: 963.65\n",
      "epoch 311(0.77)/1500(251.8) - gen_loss = 0.64447, disc_loss = 1.30772, estimated to finish: 962.68\n",
      "epoch 312(0.75)/1500(252.56) - gen_loss = 0.63124, disc_loss = 1.32223, estimated to finish: 961.66\n",
      "epoch 313(0.76)/1500(253.32) - gen_loss = 0.63404, disc_loss = 1.29728, estimated to finish: 960.67\n",
      "epoch 314(0.77)/1500(254.09) - gen_loss = 0.62767, disc_loss = 1.29711, estimated to finish: 959.72\n",
      "epoch 315(0.76)/1500(254.85) - gen_loss = 0.64869, disc_loss = 1.26649, estimated to finish: 958.73\n",
      "epoch 316(0.77)/1500(255.62) - gen_loss = 0.6441, disc_loss = 1.25182, estimated to finish: 957.77\n",
      "epoch 317(0.76)/1500(256.39) - gen_loss = 0.65681, disc_loss = 1.23546, estimated to finish: 956.8\n",
      "epoch 318(0.79)/1500(257.18) - gen_loss = 0.60017, disc_loss = 1.31778, estimated to finish: 955.93\n",
      "epoch 319(0.85)/1500(258.03) - gen_loss = 0.64655, disc_loss = 1.2667, estimated to finish: 955.28\n",
      "epoch 320(0.78)/1500(258.82) - gen_loss = 0.66275, disc_loss = 1.196, estimated to finish: 954.38\n",
      "epoch 321(0.78)/1500(259.59) - gen_loss = 0.63278, disc_loss = 1.21187, estimated to finish: 953.45\n",
      "epoch 322(0.77)/1500(260.36) - gen_loss = 0.63789, disc_loss = 1.2118, estimated to finish: 952.51\n",
      "epoch 323(0.82)/1500(261.19) - gen_loss = 0.64653, disc_loss = 1.22606, estimated to finish: 951.75\n",
      "epoch 324(0.77)/1500(261.95) - gen_loss = 0.63183, disc_loss = 1.23587, estimated to finish: 950.79\n",
      "epoch 325(0.75)/1500(262.71) - gen_loss = 0.66846, disc_loss = 1.20838, estimated to finish: 949.78\n",
      "epoch 326(0.82)/1500(263.53) - gen_loss = 0.66075, disc_loss = 1.09917, estimated to finish: 949.03\n",
      "epoch 327(0.83)/1500(264.36) - gen_loss = 0.61257, disc_loss = 1.16417, estimated to finish: 948.29\n",
      "epoch 328(0.82)/1500(265.18) - gen_loss = 0.57782, disc_loss = 1.2761, estimated to finish: 947.54\n",
      "epoch 329(0.79)/1500(265.97) - gen_loss = 0.62282, disc_loss = 1.27236, estimated to finish: 946.66\n",
      "epoch 330(0.78)/1500(266.75) - gen_loss = 0.66032, disc_loss = 1.25016, estimated to finish: 945.76\n",
      "epoch 331(0.81)/1500(267.56) - gen_loss = 0.66237, disc_loss = 1.27472, estimated to finish: 944.94\n",
      "epoch 332(0.79)/1500(268.35) - gen_loss = 0.65012, disc_loss = 1.27155, estimated to finish: 944.07\n",
      "epoch 333(0.79)/1500(269.14) - gen_loss = 0.64625, disc_loss = 1.30832, estimated to finish: 943.19\n",
      "epoch 334(0.75)/1500(269.89) - gen_loss = 0.62828, disc_loss = 1.33241, estimated to finish: 942.18\n",
      "epoch 335(0.8)/1500(270.69) - gen_loss = 0.63928, disc_loss = 1.27662, estimated to finish: 941.34\n",
      "epoch 336(0.84)/1500(271.52) - gen_loss = 0.64276, disc_loss = 1.26887, estimated to finish: 940.63\n",
      "epoch 337(0.83)/1500(272.35) - gen_loss = 0.62619, disc_loss = 1.31196, estimated to finish: 939.88\n",
      "epoch 338(0.8)/1500(273.15) - gen_loss = 0.61675, disc_loss = 1.26891, estimated to finish: 939.06\n",
      "epoch 339(0.77)/1500(273.92) - gen_loss = 0.61948, disc_loss = 1.28364, estimated to finish: 938.12\n",
      "epoch 340(0.79)/1500(274.71) - gen_loss = 0.62605, disc_loss = 1.34578, estimated to finish: 937.24\n",
      "epoch 341(0.86)/1500(275.57) - gen_loss = 0.64673, disc_loss = 1.27743, estimated to finish: 936.61\n",
      "epoch 342(0.81)/1500(276.37) - gen_loss = 0.64442, disc_loss = 1.24879, estimated to finish: 935.79\n",
      "epoch 343(0.82)/1500(277.19) - gen_loss = 0.64771, disc_loss = 1.23012, estimated to finish: 935.02\n",
      "epoch 344(0.82)/1500(278.02) - gen_loss = 0.63589, disc_loss = 1.26439, estimated to finish: 934.27\n",
      "epoch 345(0.8)/1500(278.81) - gen_loss = 0.63415, disc_loss = 1.26376, estimated to finish: 933.42\n",
      "epoch 346(0.8)/1500(279.61) - gen_loss = 0.65234, disc_loss = 1.26248, estimated to finish: 932.59\n",
      "epoch 347(0.84)/1500(280.45) - gen_loss = 0.67604, disc_loss = 1.18217, estimated to finish: 931.88\n",
      "epoch 348(0.81)/1500(281.27) - gen_loss = 0.68242, disc_loss = 1.15406, estimated to finish: 931.08\n",
      "epoch 349(0.77)/1500(282.03) - gen_loss = 0.67711, disc_loss = 1.15349, estimated to finish: 930.14\n",
      "epoch 350(0.8)/1500(282.83) - gen_loss = 0.67206, disc_loss = 1.14744, estimated to finish: 929.3\n",
      "epoch 351(0.78)/1500(283.61) - gen_loss = 0.67257, disc_loss = 1.15375, estimated to finish: 928.41\n",
      "epoch 352(0.77)/1500(284.39) - gen_loss = 0.67006, disc_loss = 1.16128, estimated to finish: 927.48\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 353(0.76)/1500(285.14) - gen_loss = 0.59719, disc_loss = 1.29331, estimated to finish: 926.52\n",
      "epoch 354(0.78)/1500(285.93) - gen_loss = 0.62623, disc_loss = 1.27426, estimated to finish: 925.63\n",
      "epoch 355(0.78)/1500(286.7) - gen_loss = 0.60171, disc_loss = 1.34417, estimated to finish: 924.72\n",
      "epoch 356(0.77)/1500(287.47) - gen_loss = 0.68042, disc_loss = 1.19429, estimated to finish: 923.78\n",
      "epoch 357(0.75)/1500(288.23) - gen_loss = 0.65127, disc_loss = 1.23464, estimated to finish: 922.8\n",
      "epoch 358(0.76)/1500(288.98) - gen_loss = 0.66845, disc_loss = 1.17988, estimated to finish: 921.84\n",
      "epoch 359(0.77)/1500(289.75) - gen_loss = 0.66086, disc_loss = 1.19223, estimated to finish: 920.9\n",
      "epoch 360(0.77)/1500(290.52) - gen_loss = 0.6517, disc_loss = 1.18498, estimated to finish: 919.99\n",
      "epoch 361(0.79)/1500(291.32) - gen_loss = 0.6374, disc_loss = 1.26798, estimated to finish: 919.14\n",
      "epoch 362(0.81)/1500(292.13) - gen_loss = 0.65328, disc_loss = 1.22731, estimated to finish: 918.35\n",
      "epoch 363(0.78)/1500(292.91) - gen_loss = 0.67395, disc_loss = 1.18699, estimated to finish: 917.47\n",
      "epoch 364(0.8)/1500(293.71) - gen_loss = 0.66046, disc_loss = 1.18697, estimated to finish: 916.65\n",
      "epoch 365(0.85)/1500(294.57) - gen_loss = 0.64826, disc_loss = 1.24108, estimated to finish: 915.98\n",
      "epoch 366(0.79)/1500(295.36) - gen_loss = 0.65502, disc_loss = 1.20317, estimated to finish: 915.12\n",
      "epoch 367(0.8)/1500(296.15) - gen_loss = 0.61863, disc_loss = 1.22548, estimated to finish: 914.27\n",
      "epoch 368(0.81)/1500(296.96) - gen_loss = 0.65458, disc_loss = 1.20619, estimated to finish: 913.48\n",
      "epoch 369(0.76)/1500(297.73) - gen_loss = 0.676, disc_loss = 1.21469, estimated to finish: 912.54\n",
      "epoch 370(0.82)/1500(298.54) - gen_loss = 0.64104, disc_loss = 1.222, estimated to finish: 911.77\n",
      "epoch 371(0.78)/1500(299.33) - gen_loss = 0.6507, disc_loss = 1.24607, estimated to finish: 910.88\n",
      "epoch 372(0.76)/1500(300.09) - gen_loss = 0.65564, disc_loss = 1.23729, estimated to finish: 909.94\n",
      "epoch 373(0.78)/1500(300.86) - gen_loss = 0.66045, disc_loss = 1.19669, estimated to finish: 909.05\n",
      "epoch 374(0.79)/1500(301.65) - gen_loss = 0.64253, disc_loss = 1.2539, estimated to finish: 908.18\n",
      "epoch 375(0.85)/1500(302.5) - gen_loss = 0.66473, disc_loss = 1.19161, estimated to finish: 907.51\n",
      "epoch 376(0.96)/1500(303.46) - gen_loss = 0.66757, disc_loss = 1.1374, estimated to finish: 907.16\n",
      "epoch 377(0.94)/1500(304.4) - gen_loss = 0.62629, disc_loss = 1.2487, estimated to finish: 906.75\n",
      "epoch 378(0.96)/1500(305.36) - gen_loss = 0.65906, disc_loss = 1.16914, estimated to finish: 906.4\n",
      "epoch 379(1.02)/1500(306.38) - gen_loss = 0.63595, disc_loss = 1.20196, estimated to finish: 906.21\n",
      "epoch 380(0.89)/1500(307.28) - gen_loss = 0.65755, disc_loss = 1.16266, estimated to finish: 905.65\n",
      "epoch 381(1.01)/1500(308.29) - gen_loss = 0.63209, disc_loss = 1.21764, estimated to finish: 905.45\n",
      "epoch 382(0.78)/1500(309.07) - gen_loss = 0.65187, disc_loss = 1.26894, estimated to finish: 904.56\n",
      "epoch 383(0.79)/1500(309.86) - gen_loss = 0.67324, disc_loss = 1.19227, estimated to finish: 903.71\n",
      "epoch 384(0.91)/1500(310.78) - gen_loss = 0.64208, disc_loss = 1.18652, estimated to finish: 903.2\n",
      "epoch 385(0.86)/1500(311.64) - gen_loss = 0.64521, disc_loss = 1.21519, estimated to finish: 902.54\n",
      "epoch 386(0.81)/1500(312.45) - gen_loss = 0.63683, disc_loss = 1.23347, estimated to finish: 901.75\n",
      "epoch 387(0.83)/1500(313.28) - gen_loss = 0.64823, disc_loss = 1.20766, estimated to finish: 900.99\n",
      "epoch 388(0.79)/1500(314.07) - gen_loss = 0.65782, disc_loss = 1.18317, estimated to finish: 900.13\n",
      "epoch 389(0.75)/1500(314.82) - gen_loss = 0.67138, disc_loss = 1.1917, estimated to finish: 899.15\n",
      "epoch 390(0.76)/1500(315.58) - gen_loss = 0.6584, disc_loss = 1.19146, estimated to finish: 898.19\n",
      "epoch 391(0.78)/1500(316.36) - gen_loss = 0.66153, disc_loss = 1.15639, estimated to finish: 897.31\n",
      "epoch 392(0.82)/1500(317.18) - gen_loss = 0.64792, disc_loss = 1.16373, estimated to finish: 896.52\n",
      "epoch 393(0.78)/1500(317.96) - gen_loss = 0.64579, disc_loss = 1.18644, estimated to finish: 895.62\n",
      "epoch 394(0.78)/1500(318.74) - gen_loss = 0.65677, disc_loss = 1.16573, estimated to finish: 894.73\n",
      "epoch 395(0.77)/1500(319.51) - gen_loss = 0.64044, disc_loss = 1.20468, estimated to finish: 893.81\n",
      "epoch 396(0.79)/1500(320.3) - gen_loss = 0.66406, disc_loss = 1.18766, estimated to finish: 892.95\n",
      "epoch 397(0.8)/1500(321.1) - gen_loss = 0.67135, disc_loss = 1.17224, estimated to finish: 892.12\n",
      "epoch 398(0.78)/1500(321.88) - gen_loss = 0.67054, disc_loss = 1.14834, estimated to finish: 891.24\n",
      "epoch 399(0.81)/1500(322.7) - gen_loss = 0.66088, disc_loss = 1.26987, estimated to finish: 890.45\n",
      "epoch 400(0.84)/1500(323.54) - gen_loss = 0.68725, disc_loss = 1.37643, estimated to finish: 889.74\n",
      "epoch 401(0.83)/1500(324.37) - gen_loss = 0.68667, disc_loss = 1.30017, estimated to finish: 888.99\n",
      "epoch 402(0.8)/1500(325.17) - gen_loss = 0.66933, disc_loss = 1.25566, estimated to finish: 888.14\n",
      "epoch 403(0.76)/1500(325.93) - gen_loss = 0.66563, disc_loss = 1.2222, estimated to finish: 887.2\n",
      "epoch 404(0.75)/1500(326.68) - gen_loss = 0.65555, disc_loss = 1.20808, estimated to finish: 886.24\n",
      "epoch 405(0.78)/1500(327.46) - gen_loss = 0.65037, disc_loss = 1.17176, estimated to finish: 885.35\n",
      "epoch 406(0.77)/1500(328.23) - gen_loss = 0.65483, disc_loss = 1.11391, estimated to finish: 884.44\n",
      "epoch 407(0.77)/1500(328.99) - gen_loss = 0.60012, disc_loss = 1.21885, estimated to finish: 883.52\n",
      "epoch 408(0.77)/1500(329.76) - gen_loss = 0.63317, disc_loss = 1.21046, estimated to finish: 882.6\n",
      "epoch 409(0.76)/1500(330.52) - gen_loss = 0.64356, disc_loss = 1.24656, estimated to finish: 881.66\n",
      "epoch 410(0.77)/1500(331.3) - gen_loss = 0.67924, disc_loss = 1.20391, estimated to finish: 880.76\n",
      "epoch 411(0.77)/1500(332.07) - gen_loss = 0.67292, disc_loss = 1.17983, estimated to finish: 879.86\n",
      "epoch 412(0.77)/1500(332.84) - gen_loss = 0.67803, disc_loss = 1.15435, estimated to finish: 878.95\n",
      "epoch 413(0.77)/1500(333.61) - gen_loss = 0.67653, disc_loss = 1.16139, estimated to finish: 878.05\n",
      "epoch 414(0.77)/1500(334.38) - gen_loss = 0.67387, disc_loss = 1.16888, estimated to finish: 877.15\n",
      "epoch 415(0.78)/1500(335.16) - gen_loss = 0.64421, disc_loss = 1.21801, estimated to finish: 876.26\n",
      "epoch 416(0.75)/1500(335.91) - gen_loss = 0.67883, disc_loss = 1.16504, estimated to finish: 875.31\n",
      "epoch 417(0.78)/1500(336.69) - gen_loss = 0.65278, disc_loss = 1.19568, estimated to finish: 874.42\n",
      "epoch 418(0.76)/1500(337.45) - gen_loss = 0.65597, disc_loss = 1.21321, estimated to finish: 873.5\n",
      "epoch 419(0.81)/1500(338.26) - gen_loss = 0.65851, disc_loss = 1.2409, estimated to finish: 872.69\n",
      "epoch 420(0.76)/1500(339.02) - gen_loss = 0.62996, disc_loss = 1.27509, estimated to finish: 871.77\n",
      "epoch 421(0.78)/1500(339.8) - gen_loss = 0.65845, disc_loss = 1.19271, estimated to finish: 870.9\n",
      "epoch 422(0.84)/1500(340.64) - gen_loss = 0.65859, disc_loss = 1.16145, estimated to finish: 870.18\n",
      "epoch 423(0.8)/1500(341.44) - gen_loss = 0.63951, disc_loss = 1.19983, estimated to finish: 869.35\n",
      "epoch 424(0.77)/1500(342.21) - gen_loss = 0.64274, disc_loss = 1.20294, estimated to finish: 868.45\n",
      "epoch 425(0.78)/1500(342.99) - gen_loss = 0.63244, disc_loss = 1.20208, estimated to finish: 867.57\n",
      "epoch 426(0.79)/1500(343.78) - gen_loss = 0.62123, disc_loss = 1.18385, estimated to finish: 866.71\n",
      "epoch 427(0.75)/1500(344.52) - gen_loss = 0.6228, disc_loss = 1.20794, estimated to finish: 865.75\n",
      "epoch 428(0.79)/1500(345.31) - gen_loss = 0.65461, disc_loss = 1.1975, estimated to finish: 864.89\n",
      "epoch 429(0.77)/1500(346.08) - gen_loss = 0.66152, disc_loss = 1.16482, estimated to finish: 864.0\n",
      "epoch 430(0.76)/1500(346.84) - gen_loss = 0.64911, disc_loss = 1.19184, estimated to finish: 863.07\n",
      "epoch 431(0.77)/1500(347.61) - gen_loss = 0.66056, disc_loss = 1.17869, estimated to finish: 862.17\n",
      "epoch 432(0.76)/1500(348.37) - gen_loss = 0.66699, disc_loss = 1.19563, estimated to finish: 861.25\n",
      "epoch 433(0.77)/1500(349.14) - gen_loss = 0.63034, disc_loss = 1.19384, estimated to finish: 860.35\n",
      "epoch 434(0.76)/1500(349.9) - gen_loss = 0.64778, disc_loss = 1.14412, estimated to finish: 859.44\n",
      "epoch 435(0.78)/1500(350.68) - gen_loss = 0.59711, disc_loss = 1.24148, estimated to finish: 858.56\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 436(0.77)/1500(351.45) - gen_loss = 0.65069, disc_loss = 1.2155, estimated to finish: 857.66\n",
      "epoch 437(0.79)/1500(352.24) - gen_loss = 0.65981, disc_loss = 1.17056, estimated to finish: 856.81\n",
      "epoch 438(0.77)/1500(353.01) - gen_loss = 0.6235, disc_loss = 1.20529, estimated to finish: 855.92\n",
      "epoch 439(0.77)/1500(353.77) - gen_loss = 0.67062, disc_loss = 1.14267, estimated to finish: 855.02\n",
      "epoch 440(0.77)/1500(354.54) - gen_loss = 0.66569, disc_loss = 1.12222, estimated to finish: 854.12\n",
      "epoch 441(0.77)/1500(355.31) - gen_loss = 0.67258, disc_loss = 1.1169, estimated to finish: 853.23\n",
      "epoch 442(0.75)/1500(356.06) - gen_loss = 0.67045, disc_loss = 1.12447, estimated to finish: 852.29\n",
      "epoch 443(0.76)/1500(356.83) - gen_loss = 0.66807, disc_loss = 1.11958, estimated to finish: 851.39\n",
      "epoch 444(0.75)/1500(357.58) - gen_loss = 0.67465, disc_loss = 1.10418, estimated to finish: 850.45\n",
      "epoch 445(0.77)/1500(358.35) - gen_loss = 0.66512, disc_loss = 1.11825, estimated to finish: 849.56\n",
      "epoch 446(0.76)/1500(359.1) - gen_loss = 0.66138, disc_loss = 1.13854, estimated to finish: 848.65\n",
      "epoch 447(0.76)/1500(359.86) - gen_loss = 0.65871, disc_loss = 1.15433, estimated to finish: 847.74\n",
      "epoch 448(0.77)/1500(360.63) - gen_loss = 0.65847, disc_loss = 1.14974, estimated to finish: 846.84\n",
      "epoch 449(0.78)/1500(361.41) - gen_loss = 0.66039, disc_loss = 1.14766, estimated to finish: 845.98\n",
      "epoch 450(0.79)/1500(362.21) - gen_loss = 0.65343, disc_loss = 1.16083, estimated to finish: 845.15\n",
      "epoch 451(0.81)/1500(363.02) - gen_loss = 0.66043, disc_loss = 1.15644, estimated to finish: 844.37\n",
      "epoch 452(0.78)/1500(363.8) - gen_loss = 0.66044, disc_loss = 1.16126, estimated to finish: 843.51\n",
      "epoch 453(0.77)/1500(364.57) - gen_loss = 0.66392, disc_loss = 1.15035, estimated to finish: 842.62\n",
      "epoch 454(0.78)/1500(365.35) - gen_loss = 0.67784, disc_loss = 1.21608, estimated to finish: 841.75\n",
      "epoch 455(0.8)/1500(366.15) - gen_loss = 0.67986, disc_loss = 1.35776, estimated to finish: 840.94\n",
      "epoch 456(0.79)/1500(366.93) - gen_loss = 0.6861, disc_loss = 1.35281, estimated to finish: 840.09\n",
      "epoch 457(0.8)/1500(367.74) - gen_loss = 0.67196, disc_loss = 1.31274, estimated to finish: 839.28\n",
      "epoch 458(0.96)/1500(368.7) - gen_loss = 0.66913, disc_loss = 1.3091, estimated to finish: 838.83\n",
      "epoch 459(0.85)/1500(369.55) - gen_loss = 0.67897, disc_loss = 1.27072, estimated to finish: 838.12\n",
      "epoch 460(0.8)/1500(370.35) - gen_loss = 0.6577, disc_loss = 1.27486, estimated to finish: 837.31\n",
      "epoch 461(0.8)/1500(371.15) - gen_loss = 0.63736, disc_loss = 1.27047, estimated to finish: 836.49\n",
      "epoch 462(0.95)/1500(372.1) - gen_loss = 0.6249, disc_loss = 1.27781, estimated to finish: 836.02\n",
      "epoch 463(0.82)/1500(372.92) - gen_loss = 0.63271, disc_loss = 1.19353, estimated to finish: 835.24\n",
      "epoch 464(0.78)/1500(373.7) - gen_loss = 0.62027, disc_loss = 1.18444, estimated to finish: 834.38\n",
      "epoch 465(0.78)/1500(374.48) - gen_loss = 0.57755, disc_loss = 1.30358, estimated to finish: 833.51\n",
      "epoch 466(0.76)/1500(375.24) - gen_loss = 0.62169, disc_loss = 1.25475, estimated to finish: 832.61\n",
      "epoch 467(0.88)/1500(376.11) - gen_loss = 0.61632, disc_loss = 1.25929, estimated to finish: 831.96\n",
      "epoch 468(0.77)/1500(376.89) - gen_loss = 0.66112, disc_loss = 1.20456, estimated to finish: 831.08\n",
      "epoch 469(0.75)/1500(377.64) - gen_loss = 0.67157, disc_loss = 1.16133, estimated to finish: 830.16\n",
      "epoch 470(0.75)/1500(378.39) - gen_loss = 0.68158, disc_loss = 1.15432, estimated to finish: 829.24\n",
      "epoch 471(0.83)/1500(379.21) - gen_loss = 0.67503, disc_loss = 1.1354, estimated to finish: 828.48\n",
      "epoch 472(0.89)/1500(380.1) - gen_loss = 0.61835, disc_loss = 1.23545, estimated to finish: 827.85\n",
      "epoch 473(0.79)/1500(380.89) - gen_loss = 0.65455, disc_loss = 1.17899, estimated to finish: 827.0\n",
      "epoch 474(0.79)/1500(381.68) - gen_loss = 0.60202, disc_loss = 1.22874, estimated to finish: 826.16\n",
      "epoch 475(0.82)/1500(382.49) - gen_loss = 0.59569, disc_loss = 1.27854, estimated to finish: 825.38\n",
      "epoch 476(0.86)/1500(383.35) - gen_loss = 0.65981, disc_loss = 1.20768, estimated to finish: 824.69\n",
      "epoch 477(0.81)/1500(384.16) - gen_loss = 0.63092, disc_loss = 1.21694, estimated to finish: 823.89\n",
      "epoch 478(0.81)/1500(384.97) - gen_loss = 0.65282, disc_loss = 1.17867, estimated to finish: 823.1\n",
      "epoch 479(0.98)/1500(385.95) - gen_loss = 0.65515, disc_loss = 1.1425, estimated to finish: 822.66\n",
      "epoch 480(0.93)/1500(386.88) - gen_loss = 0.64454, disc_loss = 1.17264, estimated to finish: 822.12\n",
      "epoch 481(0.82)/1500(387.7) - gen_loss = 0.64564, disc_loss = 1.16481, estimated to finish: 821.34\n",
      "epoch 482(0.79)/1500(388.48) - gen_loss = 0.65438, disc_loss = 1.159, estimated to finish: 820.49\n",
      "epoch 483(0.75)/1500(389.23) - gen_loss = 0.65031, disc_loss = 1.14351, estimated to finish: 819.56\n",
      "epoch 484(0.83)/1500(390.06) - gen_loss = 0.65825, disc_loss = 1.18218, estimated to finish: 818.8\n",
      "epoch 485(0.81)/1500(390.87) - gen_loss = 0.65557, disc_loss = 1.23058, estimated to finish: 818.0\n",
      "epoch 486(0.78)/1500(391.65) - gen_loss = 0.66521, disc_loss = 1.21794, estimated to finish: 817.14\n",
      "epoch 487(0.78)/1500(392.42) - gen_loss = 0.66476, disc_loss = 1.16087, estimated to finish: 816.27\n",
      "epoch 488(0.78)/1500(393.2) - gen_loss = 0.67641, disc_loss = 1.15512, estimated to finish: 815.41\n",
      "epoch 489(0.77)/1500(393.97) - gen_loss = 0.68169, disc_loss = 1.14159, estimated to finish: 814.53\n",
      "epoch 490(0.78)/1500(394.75) - gen_loss = 0.67083, disc_loss = 1.13849, estimated to finish: 813.67\n",
      "epoch 491(0.76)/1500(395.51) - gen_loss = 0.6684, disc_loss = 1.1266, estimated to finish: 812.76\n",
      "epoch 492(0.78)/1500(396.29) - gen_loss = 0.68029, disc_loss = 1.12784, estimated to finish: 811.9\n",
      "epoch 493(0.77)/1500(397.06) - gen_loss = 0.67321, disc_loss = 1.13965, estimated to finish: 811.03\n",
      "epoch 494(0.75)/1500(397.81) - gen_loss = 0.67589, disc_loss = 1.1508, estimated to finish: 810.12\n",
      "epoch 495(0.77)/1500(398.58) - gen_loss = 0.67817, disc_loss = 1.1428, estimated to finish: 809.25\n",
      "epoch 496(0.77)/1500(399.35) - gen_loss = 0.67786, disc_loss = 1.16166, estimated to finish: 808.37\n",
      "epoch 497(0.75)/1500(400.1) - gen_loss = 0.65066, disc_loss = 1.23351, estimated to finish: 807.45\n",
      "epoch 498(0.77)/1500(400.87) - gen_loss = 0.66749, disc_loss = 1.23784, estimated to finish: 806.57\n",
      "epoch 499(0.75)/1500(401.62) - gen_loss = 0.69169, disc_loss = 1.0863, estimated to finish: 805.65\n",
      "epoch 500(0.8)/1500(402.42) - gen_loss = 0.69232, disc_loss = 1.02769, estimated to finish: 804.84\n",
      "epoch 501(0.8)/1500(403.22) - gen_loss = 0.69177, disc_loss = 1.01714, estimated to finish: 804.03\n",
      "epoch 502(0.78)/1500(404.0) - gen_loss = 0.68745, disc_loss = 1.02466, estimated to finish: 803.18\n",
      "epoch 503(0.82)/1500(404.83) - gen_loss = 0.68304, disc_loss = 1.02994, estimated to finish: 802.41\n",
      "epoch 504(0.76)/1500(405.58) - gen_loss = 0.68687, disc_loss = 1.02604, estimated to finish: 801.51\n",
      "epoch 505(0.76)/1500(406.34) - gen_loss = 0.68577, disc_loss = 1.026, estimated to finish: 800.62\n",
      "epoch 506(0.75)/1500(407.09) - gen_loss = 0.55592, disc_loss = 1.23767, estimated to finish: 799.7\n",
      "epoch 507(0.78)/1500(407.87) - gen_loss = 0.55966, disc_loss = 1.27326, estimated to finish: 798.85\n",
      "epoch 508(0.76)/1500(408.64) - gen_loss = 0.63431, disc_loss = 1.2189, estimated to finish: 797.97\n",
      "epoch 509(0.77)/1500(409.4) - gen_loss = 0.65856, disc_loss = 1.19187, estimated to finish: 797.09\n",
      "epoch 510(0.76)/1500(410.16) - gen_loss = 0.64739, disc_loss = 1.20773, estimated to finish: 796.2\n",
      "epoch 511(0.75)/1500(410.92) - gen_loss = 0.65576, disc_loss = 1.18541, estimated to finish: 795.29\n",
      "epoch 512(0.81)/1500(411.73) - gen_loss = 0.64809, disc_loss = 1.16209, estimated to finish: 794.5\n",
      "epoch 513(0.8)/1500(412.53) - gen_loss = 0.65836, disc_loss = 1.08877, estimated to finish: 793.69\n",
      "epoch 514(0.83)/1500(413.36) - gen_loss = 0.59267, disc_loss = 1.22702, estimated to finish: 792.94\n",
      "epoch 515(0.81)/1500(414.17) - gen_loss = 0.65789, disc_loss = 1.15721, estimated to finish: 792.15\n",
      "epoch 516(0.79)/1500(414.96) - gen_loss = 0.65197, disc_loss = 1.15653, estimated to finish: 791.33\n",
      "epoch 517(0.77)/1500(415.74) - gen_loss = 0.66464, disc_loss = 1.14347, estimated to finish: 790.46\n",
      "epoch 518(0.81)/1500(416.54) - gen_loss = 0.63651, disc_loss = 1.18714, estimated to finish: 789.67\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 519(0.78)/1500(417.32) - gen_loss = 0.633, disc_loss = 1.17229, estimated to finish: 788.82\n",
      "epoch 520(0.8)/1500(418.13) - gen_loss = 0.66266, disc_loss = 1.18482, estimated to finish: 788.01\n",
      "epoch 521(0.91)/1500(419.04) - gen_loss = 0.63463, disc_loss = 1.1983, estimated to finish: 787.4\n",
      "epoch 522(0.75)/1500(419.79) - gen_loss = 0.66505, disc_loss = 1.17654, estimated to finish: 786.5\n",
      "epoch 523(0.76)/1500(420.55) - gen_loss = 0.6729, disc_loss = 1.14309, estimated to finish: 785.61\n",
      "epoch 524(0.78)/1500(421.33) - gen_loss = 0.64201, disc_loss = 1.17328, estimated to finish: 784.76\n",
      "epoch 525(0.82)/1500(422.15) - gen_loss = 0.65405, disc_loss = 1.16596, estimated to finish: 783.99\n",
      "epoch 526(0.81)/1500(422.95) - gen_loss = 0.67865, disc_loss = 1.11229, estimated to finish: 783.19\n",
      "epoch 527(0.8)/1500(423.75) - gen_loss = 0.67287, disc_loss = 1.09595, estimated to finish: 782.37\n",
      "epoch 528(0.81)/1500(424.56) - gen_loss = 0.67299, disc_loss = 1.07686, estimated to finish: 781.58\n",
      "epoch 529(0.79)/1500(425.35) - gen_loss = 0.67032, disc_loss = 1.07541, estimated to finish: 780.75\n",
      "epoch 530(0.98)/1500(426.33) - gen_loss = 0.62228, disc_loss = 1.18706, estimated to finish: 780.26\n",
      "epoch 531(0.92)/1500(427.25) - gen_loss = 0.64315, disc_loss = 1.15273, estimated to finish: 779.67\n",
      "epoch 532(0.85)/1500(428.1) - gen_loss = 0.65418, disc_loss = 1.15967, estimated to finish: 778.95\n",
      "epoch 533(0.79)/1500(428.89) - gen_loss = 0.65373, disc_loss = 1.15027, estimated to finish: 778.12\n",
      "epoch 534(0.81)/1500(429.7) - gen_loss = 0.66127, disc_loss = 1.18763, estimated to finish: 777.32\n",
      "epoch 535(0.99)/1500(430.69) - gen_loss = 0.64808, disc_loss = 1.199, estimated to finish: 776.85\n",
      "epoch 536(0.87)/1500(431.56) - gen_loss = 0.65601, disc_loss = 1.20818, estimated to finish: 776.16\n",
      "epoch 537(0.83)/1500(432.39) - gen_loss = 0.66628, disc_loss = 1.15152, estimated to finish: 775.4\n",
      "epoch 538(0.81)/1500(433.2) - gen_loss = 0.67903, disc_loss = 1.13924, estimated to finish: 774.61\n",
      "epoch 539(0.77)/1500(433.98) - gen_loss = 0.66915, disc_loss = 1.14895, estimated to finish: 773.75\n",
      "epoch 540(0.79)/1500(434.76) - gen_loss = 0.65546, disc_loss = 1.15383, estimated to finish: 772.91\n",
      "epoch 541(0.75)/1500(435.51) - gen_loss = 0.66589, disc_loss = 1.12851, estimated to finish: 772.01\n",
      "epoch 542(0.75)/1500(436.27) - gen_loss = 0.67033, disc_loss = 1.07933, estimated to finish: 771.11\n",
      "epoch 543(0.75)/1500(437.01) - gen_loss = 0.64209, disc_loss = 1.11808, estimated to finish: 770.2\n",
      "epoch 544(0.76)/1500(437.77) - gen_loss = 0.63141, disc_loss = 1.16322, estimated to finish: 769.32\n",
      "epoch 545(0.78)/1500(438.54) - gen_loss = 0.64839, disc_loss = 1.14981, estimated to finish: 768.46\n",
      "epoch 546(0.89)/1500(439.43) - gen_loss = 0.65531, disc_loss = 1.16329, estimated to finish: 767.8\n",
      "epoch 547(0.8)/1500(440.23) - gen_loss = 0.66901, disc_loss = 1.14419, estimated to finish: 766.99\n",
      "epoch 548(0.85)/1500(441.08) - gen_loss = 0.66308, disc_loss = 1.14191, estimated to finish: 766.26\n",
      "epoch 549(0.82)/1500(441.9) - gen_loss = 0.66886, disc_loss = 1.13406, estimated to finish: 765.47\n",
      "epoch 550(0.89)/1500(442.79) - gen_loss = 0.67171, disc_loss = 1.12703, estimated to finish: 764.81\n",
      "epoch 551(0.76)/1500(443.55) - gen_loss = 0.6662, disc_loss = 1.14745, estimated to finish: 763.93\n",
      "epoch 552(0.86)/1500(444.41) - gen_loss = 0.66471, disc_loss = 1.12194, estimated to finish: 763.23\n",
      "epoch 553(0.99)/1500(445.41) - gen_loss = 0.65956, disc_loss = 1.1793, estimated to finish: 762.75\n",
      "epoch 554(0.81)/1500(446.21) - gen_loss = 0.68132, disc_loss = 1.08942, estimated to finish: 761.94\n",
      "epoch 555(1.03)/1500(447.24) - gen_loss = 0.66778, disc_loss = 1.09759, estimated to finish: 761.52\n",
      "epoch 556(0.96)/1500(448.2) - gen_loss = 0.66906, disc_loss = 1.08296, estimated to finish: 760.97\n",
      "epoch 557(0.78)/1500(448.98) - gen_loss = 0.61582, disc_loss = 1.20352, estimated to finish: 760.12\n",
      "epoch 558(0.8)/1500(449.78) - gen_loss = 0.63457, disc_loss = 1.18096, estimated to finish: 759.3\n",
      "epoch 559(0.77)/1500(450.55) - gen_loss = 0.65492, disc_loss = 1.17284, estimated to finish: 758.44\n",
      "epoch 560(0.78)/1500(451.34) - gen_loss = 0.64847, disc_loss = 1.197, estimated to finish: 757.6\n",
      "epoch 561(0.77)/1500(452.11) - gen_loss = 0.67075, disc_loss = 1.14156, estimated to finish: 756.74\n",
      "epoch 562(0.76)/1500(452.87) - gen_loss = 0.64508, disc_loss = 1.2098, estimated to finish: 755.86\n",
      "epoch 563(0.76)/1500(453.63) - gen_loss = 0.66586, disc_loss = 1.17019, estimated to finish: 754.98\n",
      "epoch 564(0.95)/1500(454.59) - gen_loss = 0.65976, disc_loss = 1.19553, estimated to finish: 754.42\n",
      "epoch 565(0.95)/1500(455.54) - gen_loss = 0.66595, disc_loss = 1.20387, estimated to finish: 753.86\n",
      "epoch 566(1.0)/1500(456.55) - gen_loss = 0.63779, disc_loss = 1.21431, estimated to finish: 753.38\n",
      "epoch 567(0.83)/1500(457.37) - gen_loss = 0.62591, disc_loss = 1.23988, estimated to finish: 752.61\n",
      "epoch 568(0.8)/1500(458.18) - gen_loss = 0.66483, disc_loss = 1.16856, estimated to finish: 751.8\n",
      "epoch 569(0.77)/1500(458.95) - gen_loss = 0.64818, disc_loss = 1.22849, estimated to finish: 750.93\n",
      "epoch 570(0.75)/1500(459.7) - gen_loss = 0.62854, disc_loss = 1.19598, estimated to finish: 750.03\n",
      "epoch 571(0.76)/1500(460.46) - gen_loss = 0.64699, disc_loss = 1.19455, estimated to finish: 749.15\n",
      "epoch 572(0.76)/1500(461.22) - gen_loss = 0.63766, disc_loss = 1.17377, estimated to finish: 748.27\n",
      "epoch 573(0.75)/1500(461.97) - gen_loss = 0.6696, disc_loss = 1.1492, estimated to finish: 747.38\n",
      "epoch 574(0.76)/1500(462.73) - gen_loss = 0.65955, disc_loss = 1.15724, estimated to finish: 746.5\n",
      "epoch 575(0.76)/1500(463.49) - gen_loss = 0.64978, disc_loss = 1.19674, estimated to finish: 745.62\n",
      "epoch 576(0.76)/1500(464.25) - gen_loss = 0.65353, disc_loss = 1.20446, estimated to finish: 744.73\n",
      "epoch 577(0.76)/1500(465.0) - gen_loss = 0.62607, disc_loss = 1.25254, estimated to finish: 743.84\n",
      "epoch 578(0.76)/1500(465.76) - gen_loss = 0.67147, disc_loss = 1.15681, estimated to finish: 742.96\n",
      "epoch 579(0.76)/1500(466.52) - gen_loss = 0.66846, disc_loss = 1.15747, estimated to finish: 742.08\n",
      "epoch 580(0.76)/1500(467.28) - gen_loss = 0.68136, disc_loss = 1.12728, estimated to finish: 741.2\n",
      "epoch 581(0.76)/1500(468.04) - gen_loss = 0.63284, disc_loss = 1.20402, estimated to finish: 740.33\n",
      "epoch 582(0.9)/1500(468.94) - gen_loss = 0.64666, disc_loss = 1.20384, estimated to finish: 739.67\n",
      "epoch 583(1.05)/1500(469.99) - gen_loss = 0.6514, disc_loss = 1.16937, estimated to finish: 739.25\n",
      "epoch 584(0.93)/1500(470.92) - gen_loss = 0.65285, disc_loss = 1.18539, estimated to finish: 738.64\n",
      "epoch 585(0.96)/1500(471.88) - gen_loss = 0.65971, disc_loss = 1.18364, estimated to finish: 738.07\n",
      "epoch 586(1.04)/1500(472.93) - gen_loss = 0.6209, disc_loss = 1.2445, estimated to finish: 737.64\n",
      "epoch 587(1.09)/1500(474.02) - gen_loss = 0.64107, disc_loss = 1.197, estimated to finish: 737.27\n",
      "epoch 588(1.03)/1500(475.05) - gen_loss = 0.65219, disc_loss = 1.1962, estimated to finish: 736.81\n",
      "epoch 589(1.06)/1500(476.11) - gen_loss = 0.66597, disc_loss = 1.17166, estimated to finish: 736.4\n",
      "epoch 590(0.83)/1500(476.94) - gen_loss = 0.66056, disc_loss = 1.1379, estimated to finish: 735.61\n",
      "epoch 591(0.92)/1500(477.85) - gen_loss = 0.66896, disc_loss = 1.14621, estimated to finish: 734.97\n",
      "epoch 592(0.99)/1500(478.85) - gen_loss = 0.65594, disc_loss = 1.18481, estimated to finish: 734.45\n",
      "epoch 593(0.9)/1500(479.75) - gen_loss = 0.65703, disc_loss = 1.18453, estimated to finish: 733.78\n",
      "epoch 594(0.86)/1500(480.61) - gen_loss = 0.65062, disc_loss = 1.16154, estimated to finish: 733.05\n",
      "epoch 595(0.82)/1500(481.43) - gen_loss = 0.64614, disc_loss = 1.27342, estimated to finish: 732.26\n",
      "epoch 596(0.88)/1500(482.31) - gen_loss = 0.66809, disc_loss = 1.21748, estimated to finish: 731.55\n",
      "epoch 597(0.81)/1500(483.11) - gen_loss = 0.63094, disc_loss = 1.22792, estimated to finish: 730.74\n",
      "epoch 598(0.8)/1500(483.91) - gen_loss = 0.65166, disc_loss = 1.19006, estimated to finish: 729.92\n",
      "epoch 599(1.11)/1500(485.02) - gen_loss = 0.6593, disc_loss = 1.20512, estimated to finish: 729.56\n",
      "epoch 600(1.11)/1500(486.14) - gen_loss = 0.63925, disc_loss = 1.19078, estimated to finish: 729.21\n",
      "epoch 601(1.07)/1500(487.21) - gen_loss = 0.67142, disc_loss = 1.15883, estimated to finish: 728.78\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 602(0.91)/1500(488.11) - gen_loss = 0.64867, disc_loss = 1.19611, estimated to finish: 728.12\n",
      "epoch 603(0.81)/1500(488.92) - gen_loss = 0.66238, disc_loss = 1.16204, estimated to finish: 727.3\n",
      "epoch 604(0.79)/1500(489.71) - gen_loss = 0.64483, disc_loss = 1.18892, estimated to finish: 726.46\n",
      "epoch 605(0.79)/1500(490.5) - gen_loss = 0.65828, disc_loss = 1.17718, estimated to finish: 725.62\n",
      "epoch 606(0.87)/1500(491.37) - gen_loss = 0.63986, disc_loss = 1.18909, estimated to finish: 724.89\n",
      "epoch 607(0.88)/1500(492.25) - gen_loss = 0.6578, disc_loss = 1.16287, estimated to finish: 724.18\n",
      "epoch 608(0.77)/1500(493.01) - gen_loss = 0.64234, disc_loss = 1.15794, estimated to finish: 723.3\n",
      "epoch 609(0.82)/1500(493.83) - gen_loss = 0.65286, disc_loss = 1.12969, estimated to finish: 722.5\n",
      "epoch 610(0.9)/1500(494.73) - gen_loss = 0.65109, disc_loss = 1.1362, estimated to finish: 721.82\n",
      "epoch 611(0.9)/1500(495.63) - gen_loss = 0.61571, disc_loss = 1.24335, estimated to finish: 721.14\n",
      "epoch 612(0.84)/1500(496.48) - gen_loss = 0.66795, disc_loss = 1.21223, estimated to finish: 720.38\n",
      "epoch 613(0.84)/1500(497.31) - gen_loss = 0.65253, disc_loss = 1.17476, estimated to finish: 719.6\n",
      "epoch 614(0.84)/1500(498.15) - gen_loss = 0.65936, disc_loss = 1.1558, estimated to finish: 718.83\n",
      "epoch 615(0.76)/1500(498.91) - gen_loss = 0.67732, disc_loss = 1.12752, estimated to finish: 717.94\n",
      "epoch 616(0.9)/1500(499.8) - gen_loss = 0.63407, disc_loss = 1.2285, estimated to finish: 717.25\n",
      "epoch 617(0.77)/1500(500.57) - gen_loss = 0.63576, disc_loss = 1.20008, estimated to finish: 716.38\n",
      "epoch 618(0.8)/1500(501.38) - gen_loss = 0.67527, disc_loss = 1.1455, estimated to finish: 715.56\n",
      "epoch 619(0.81)/1500(502.18) - gen_loss = 0.66341, disc_loss = 1.1234, estimated to finish: 714.74\n",
      "epoch 620(0.77)/1500(502.96) - gen_loss = 0.66593, disc_loss = 1.1569, estimated to finish: 713.88\n",
      "epoch 621(0.92)/1500(503.88) - gen_loss = 0.66879, disc_loss = 1.14904, estimated to finish: 713.22\n",
      "epoch 622(0.92)/1500(504.79) - gen_loss = 0.67423, disc_loss = 1.1236, estimated to finish: 712.55\n",
      "epoch 623(0.86)/1500(505.66) - gen_loss = 0.65733, disc_loss = 1.16533, estimated to finish: 711.81\n",
      "epoch 624(0.78)/1500(506.44) - gen_loss = 0.65533, disc_loss = 1.13478, estimated to finish: 710.96\n",
      "epoch 625(0.95)/1500(507.39) - gen_loss = 0.66905, disc_loss = 1.124, estimated to finish: 710.34\n",
      "epoch 626(0.8)/1500(508.18) - gen_loss = 0.67247, disc_loss = 1.10335, estimated to finish: 709.51\n",
      "epoch 627(0.87)/1500(509.05) - gen_loss = 0.65291, disc_loss = 1.13115, estimated to finish: 708.78\n",
      "epoch 628(0.83)/1500(509.88) - gen_loss = 0.6527, disc_loss = 1.11678, estimated to finish: 707.99\n",
      "epoch 629(1.02)/1500(510.91) - gen_loss = 0.6414, disc_loss = 1.1492, estimated to finish: 707.47\n",
      "epoch 630(0.78)/1500(511.68) - gen_loss = 0.66352, disc_loss = 1.15693, estimated to finish: 706.61\n",
      "epoch 631(0.94)/1500(512.62) - gen_loss = 0.63207, disc_loss = 1.25185, estimated to finish: 705.97\n",
      "epoch 632(0.83)/1500(513.46) - gen_loss = 0.65247, disc_loss = 1.15341, estimated to finish: 705.19\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-f8d5ec065379>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseqs_normal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-30-d86331dad080>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, epochs, save_path)\u001b[0m\n\u001b[1;32m     54\u001b[0m                 seqs_noised[self.key] = np.random.normal(max_s / 2.0, max_s / 10.0,\n\u001b[1;32m     55\u001b[0m                                                          size=(current_seqs.shape[0])).astype('float32')\n\u001b[0;32m---> 56\u001b[0;31m                 \u001b[0mgen_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisc_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_seqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseqs_noised\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m                 \u001b[0mtotal_gen_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mgen_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m                 \u001b[0mtotal_disc_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdisc_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    778\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    805\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    806\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 807\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    808\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2829\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2831\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1846\u001b[0m                            resource_variable_ops.BaseResourceVariable))],\n\u001b[1;32m   1847\u001b[0m         \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1848\u001b[0;31m         cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1850\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1922\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1923\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1924\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1926\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    548\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = Train(seqs_normal, adj, node_f, 1500, 9)\n",
    "train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'seqs_normal' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-e99af09f9db6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mseqs_normal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m96\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;31m# for w in range(1,n):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m#     generated_seq = train.generate()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'seqs_normal' is not defined"
     ]
    }
   ],
   "source": [
    "from matplotlib.pyplot import *\n",
    "fig, ax = subplots()\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(10)\n",
    "n=100\n",
    "key=9\n",
    "for w in range(0,n):\n",
    "    seqs_normal[w:w+96][key].plot(ax=ax) \n",
    "# for w in range(1,n):\n",
    "#     generated_seq = train.generate()\n",
    "#     generated_seq.plot(ax=ax)\n",
    "ax.legend(['real'+str(w) for w in range(1,n)]);# +['gen'+str(w) for w in range(1,n)]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 3, 3), dtype=int32, numpy=\n",
       "array([[[0, 3, 6],\n",
       "        [1, 4, 7],\n",
       "        [2, 5, 8]]])>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "fc=tf.convert_to_tensor([[0,3,6],\n",
    "    [1,4,7],\n",
    "    [2,5,8]])\n",
    "tf.expand_dims(fc, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "Input to reshape is a tensor with 9 values, but the requested shape has 27 [Op:Reshape]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-ce2e2b4acd37>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     [2,5,8]])\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mReshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1001\u001b[0m         with autocast_variable.enable_auto_cast_variables(\n\u001b[0;32m   1002\u001b[0m             self._compute_dtype_object):\n\u001b[1;32m-> 1003\u001b[1;33m           \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1005\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\keras\\layers\\core.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m    549\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    550\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 551\u001b[1;33m     result = array_ops.reshape(\n\u001b[0m\u001b[0;32m    552\u001b[0m         inputs, (array_ops.shape(inputs)[0],) + self.target_shape)\n\u001b[0;32m    553\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    199\u001b[0m     \u001b[1;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 201\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    202\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    203\u001b[0m       \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36mreshape\u001b[1;34m(tensor, shape, name)\u001b[0m\n\u001b[0;32m    193\u001b[0m     \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m \u001b[0mHas\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msame\u001b[0m \u001b[0mtype\u001b[0m \u001b[1;32mas\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m   \"\"\"\n\u001b[1;32m--> 195\u001b[1;33m   \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    196\u001b[0m   \u001b[0mtensor_util\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmaybe_set_static_shape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\ops\\gen_array_ops.py\u001b[0m in \u001b[0;36mreshape\u001b[1;34m(tensor, shape, name)\u001b[0m\n\u001b[0;32m   8132\u001b[0m       \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   8133\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 8134\u001b[1;33m       return reshape_eager_fallback(\n\u001b[0m\u001b[0;32m   8135\u001b[0m           tensor, shape, name=name, ctx=_ctx)\n\u001b[0;32m   8136\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_SymbolicException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\ops\\gen_array_ops.py\u001b[0m in \u001b[0;36mreshape_eager_fallback\u001b[1;34m(tensor, shape, name, ctx)\u001b[0m\n\u001b[0;32m   8157\u001b[0m   \u001b[0m_inputs_flat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   8158\u001b[0m   \u001b[0m_attrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"T\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_attr_T\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Tshape\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_attr_Tshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 8159\u001b[1;33m   _result = _execute.execute(b\"Reshape\", 1, inputs=_inputs_flat, attrs=_attrs,\n\u001b[0m\u001b[0;32m   8160\u001b[0m                              ctx=ctx, name=name)\n\u001b[0;32m   8161\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0m_execute\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmust_record_gradient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m: Input to reshape is a tensor with 9 values, but the requested shape has 27 [Op:Reshape]"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras.layers as layers\n",
    "\n",
    "fc=tf.convert_to_tensor([[0,3,6],\n",
    "    [1,4,7],\n",
    "    [2,5,8]])\n",
    "layers. layers.Reshape((3,1),input_shape=(3,))(fc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1,fc.shape[0],fc.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
